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The informationalization of race: communication technologies and genomics in the information age

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THE INFORMATIONALIZATION OF RACE:
COMMUNICATION TECHNOLOGIES AND GENOMICS IN THE
INFORMATION AGE

by

Peter A. Chow-White

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMMUNICATION)

August 2007

Copyright 2007 Peter A. Chow-White

ii
Acknowledgements

While a project like this bears the name of a single author, an enormous number of
people have contributed either directly or indirectly to the conceptual, research, and
writing processes. I apologize in advance for any omissions that may follow. First,
some random thank you’s. The various coffee shops in Pasadena where Paulina and I
made our second offices. Richard, Susanne, and Celine at the local public library.
The community at the most wonderful Pacific Oaks Children’s School. Doctor James
Henry, Toby Miller, Joe Turow. The city of Los Angeles: you grew on me and
became home to my family.
I would like to thank the members of the HapMap Project who graciously
gave their valuable time to answer my questions about different aspects of their
work. Besides providing much of the empirical data that informed many parts of this
dissertation, I learned a great deal more about what questions to ask about my own
work. In addition, there were a number of others working the genomics and society
space who took my cold calls and emails from the early stages of the research. They
were patient with my clumsy questions in the beginning and encouraging as my ideas
took shape.
From my first day of orientation at Annenberg, members of my cohort, Matt
Zhou, Namkee Park, Lu Tang, Peng Wei, Li Ying, Sangeeta Fernandes, Anna
Kostygina, Holley Wilkin, and Cynthia Willis, have been an important source of
community. We made it through core together, had numerous group dinners
(beginning with Thanksgiving at Sizzler on Figueroa), and are now making our way

iii
into life after Annenberg. I wish each of them the best and look forward to seeing
everyone at ICA in the future.
Two people served double duty as good friends and unofficial committee
members. Jack Qiu and Sandy Green both lent an ear and intellectual council when
the threads that became this dissertation converged in my mind. Jack listened intently
as I ran my budding ideas about the changing nature of race and the information age
by him on the patio at Annenberg. When I finished, he brought all of them together
and suggested the concept, the “informationalization of race.” I have him to thank (or
to blame) for that gift. Sandy was the first person I babbled on to about my ideas
about new communication technologies being used across social institutions on the
phone as I cleaned up my garage in Pasadena that warm spring night. Unfortunately
for him, he continued to listen through all stages of the work, on the phone, at the
Mud Puppy, in the Vortex.
The Annenberg School for Communication provided the education and
funding during my tenure at the University of Southern California. From the
administrative to the tech teams, the staff was always professional and helpful in a
dynamic environment. The professors encouraged an open door policy with students
and I always felt someone would be there to guide my questions. This especially
goes to Sandra Ball-Rokeach, Doug Thomas, Sarah Banet-Weiser, Peter Monge,
Patti Riley, Larry Gross, Felix Gutierrez, Vincent Brook, and Dean Geoff Cowan.
The Annenberg I applied to looked very different from the Annenberg I graduated
from as the school underwent enormous changes during those years. I hope that their

iv
growth, continued dedication to excellence, and new approach to and expectations of
doctoral students does not come at the cost to their commitment to community that
was made in the early stages of my studies.
My dissertation committee nurtured this project at every stage. I was very
fortunate to work with George Sanchez, a central figure in the rise of the LA School.
In his course, Reading Los Angeles, I learned the value of knowing intellectual
positions that are different from my own. In my work, he reminded me that there is a
larger audience out there that I need to address. And on the tram to Union Station
one day, he told me about kids and the double helix. Marita Sturken went above and
beyond the duties of a committee member. When I was ready, she would be there to
listen, ask me difficult questions, and be the meeting point for different aspects of
my work. I always felt she cared about the research and me. I am deeply grateful for
everything she has done and continues to do for my family, even when she moved to
New York. Manuel Castells joined Annenberg at a pivotal point in my studies. I was
a number of firsts for him at ASC: first qualifying committee and first dissertation.
When he engages students one on one, it is an unforgettable experience. While we
were geographically separate during the latter stage of the dissertation, the
interactions continued to be productive and he saw me through to the end.
Oddly, the most important people are the last ones to mention. Sybil
Tolentino became one of the family over our years in LA. Anna Chow made
numerous trips to stay with us. It would have been very difficult to accomplish what
we did without their help. Thank you Mum and Dad, Jeremy, Ben, Jess, and Emma.
Even though we were spread all over the globe, you have always been my home.

v
Callum and Finlay were born during my PhD studies. They both gave me a sense of
direction and purpose, a kick in the butt into adulthood really. Countless trips to the
parks of Pasadena, to Pacific Oaks, and around the great city of Los Angeles helped
me realize that life cannot only be about the mind. They make my work and me
better. Finally, none of this would have been possible without the support of Paulina.
I am lucky to be in the trenches with her. She is my partner in life intellectually,
politically, and emotionally. I sincerely appreciate everything you’ve done. Thank
you, Paulina.

vi

Table of Contents

Acknowledgements ii

Abstract viii

Chapter 1: Introduction 1
Research Questions and Rationale 7
Methodology 11
Makeup of Interviewees 14
Makeup and Analysis of Textual Data 15
Chapter Overview 16
A Brief Introduction to the HapMap Project and Genomics 18

Chapter 2: The Informationalization of Race: Communication
Technologies and the New Conditions of Racialization

25
Race, Racialization, and Colorblindness 25
From Race Relations to Racial Projects and Racialization 27
Beyond Black and White: Racial Triangulation 33
The New Racial Order: Colorblindness 35
Central Frames of Colorblindness 39
The Information Age and New Media Technologies 43
Race and Technology 54
The Information Society, Critical Race, and Science and Technology
Studies

57
The Informationalization of Race 62
Conclusion 67

Chapter 3: Technogenomics: The Digital Shaping of Biology and the Rise
of the Database

69
From Analytics to Synthetics, From Wet to Dry Labs: Biology
Becomes an Information Science
73
Data Mining the Code of Human Life 83
Databases, dbSNP, and the DNA Banking System 85
HapMap Databases and the Turn to Difference 91
The Role of the Internet in Networking Knowledge Production, Open
Access to Data, and Informed Consent

96
Networking and Distribution 96
Genome data as a public good: Democratizing the data through the
public domain

102
Techno-consent and community engagement 106
Conclusion 110

vii
Chapter 4: The Legal and Institutional Formation of Biotechnology and
Genomics, 1977-2004

112
The Legal Signal for the Commercialization of Biotechnology:
Diamond vs. Chakrabarty

116
Deregulating University-Industrial Relations 118
Conflict of Interest Policies in biomedical and Science Journals 120
Color Consciousness in a Time of Colorblindness: OMB Directive 15,
NIH Revitalization Act of 1993, and Editorial Policies on Race and
Ethnicity

123
OMB Directive 15 125
The NIH Revitalization Act of 1993 131
Editorial Policies on Race and Ethnicity (EPRE), 1991-2004 134
Conclusion 146

Chapter 5: Discursive Formations of the Informationalization of Race:
Race Talk in Genomics and the HapMap Project

149
Definitions of Race in Genomics 157
Planetary Humanism 161
Out of Africa: The Single Origin Story 162
We are All 99.9% the Same 164
The Within/Between Debate 165
Does Race Exist? 166
De-Racializing the Genome: From Proxy to Precision 172
Strategic Essentialism 177
Colorblind Talk in Genomics 181
The Turn to Racial Realism 185
“I am a Racial Profiling Doctor” 186
Misinterpretation of the Data 189
Conclusion 191

Chapter 6: Conclusion 194
Race and the Work of Information in the Age of the Digital Database 199
The Informationalization of Race: A Cultural Theory of Technology
and Identity

204

Bibliography 207

Appendices
Appendix A: Models of Racial Discourse 233
Appendix B: Code for Interviews 234

viii
ABSTRACT

As a mode of representation, a structuring device, and as a biological category, race
is undergoing a significant transformation in the digital age. This dissertation shows
how a new form of racialization is being produced through developments and
innovations in communication technologies. Increasingly, racial knowledge is being
constructed from seemingly neutral and unrelated pieces of information, which are
collected, sorted, analyzed, and accessed through two key technologies: databases
and the Internet. I call this interaction between technology and identity the
informationalization of race. Race as information develops from race as the body
and race as culture. To understand how this new formation is emerging through the
social shaping of new media technologies in a specific institutional setting, I conduct
organizational, political economic, and discourse analyses of the next Human
Genome Project, the HapMap Project. Advances in human genomics has recently re-
invigorated scientific research into the relationship between race and biology. Where
the HGP concluded that humanity is similar at the genetic level, the HapMap Project
began by looking for differences between racialized groups. The findings from the
HapMap project have been promised to help in developing pharmaceuticals that can
target common diseases, such as cancer. However, this development also opens the
door to old biological conceptions of race and a new phase of the biopolitics where
biology, technology, and information converge on the human body.

1
Chapter 1
Introduction
Something is happening to race.
(Fausto-Sterling 2003:1)

In our time – at the end of the twentieth century – the crisis of race in
America is still raging… In this age of globalization, with its impressive
scientific and technological innovations in information, communication, and
applied biology, a focus on the lingering effects of racism seems outdated and
antiquated. The global cultural bazaar of entertainment and enjoyment, the
global shopping mall of advertising and marketing, the global workplace of
blue-collar and white-collar employment, and the global financial network of
computerized transactions and megacorporate mergers appear to render any
talk about race irrelevant.
(Gates and West 1997:68)

Three independent processes emerged in the 1970s that would converge on the most
microscopic level of human life: one social, one technological, and one scientific.
The Civil Rights Movement challenged and changed fundamental assumptions about
the organization of society and the meaning of identity. The protests and acts of civil
disobedience of the 1960s that characterized the movement fought for and won
political participation for African Americans and other minorities with the signing of
the Voting Rights Act of 1965. Along with this landmark legislation, the Civil Rights
Act, the Fair Housing Act of 1968, and the Supreme Court’s 1967 ruling on Loving
v. Virginia, which ruled anti-miscegenation laws unconstitutional, ushered in a new
era of social organization. The movement for racial equality shifted from issues of
political participation to issues of access to education, workplace discrimination, and
representation in the 1970s. The focus for action shifted to other social structures,
such as busing programs and affirmative action, and culture, such as changing the

2
portrayal of blacks and Asians in the media, in an effort to further challenge systemic
racism. The end of the civil rights movement signaled a shift, but not an end, in the
organization of race and expression of racism in society. The prevailing racial
paradigm began to change from a society structured in dominance and Jim Crow to
an ideology of colorblindness. While the first process attacked an old social system,
the other two sought to build something new.
The second trend that emerged in the 1970s was the creation of a new
technological paradigm in information and communication technologies that was the
cumulation of a number of technological discoveries that were independent but fed
back and built on one another. The key technologies were the microprocessor, the
microcomputer, telecommunications, and software, each clustering with another.
Advances in each technology made possible further advances in others. For example,
the microcomputer was made possible by the microprocessor. The computer also
relied on developments in telecommunications to facilitate its networks, making
them more powerful and flexible. Many of the individual developments are all parts
of computing and digital technology that we largely take for granted today. When
this new communication system came into existence through the diffusion of
computers and information networks in all levels of business, government,
education, and the home, its shaping and diffusion was dependent on the cultural,
political, and economic historical context. These new technologies and their
diffusion and contribution to the organization of society are to the information age
like electricity was to the industrial revolution.

3
Advancements in genetic engineering instigated the third process. Equally as
revolutionary, it has parallels with the new information technology paradigm as it
took place in similar locations, such as the Silicon Valley, Maryland, the research
triangle in North Carolina, and the Boston/Cambridge area. The discovery of
recombinant DNA (rDNA) technology in 1973 transformed the biological sciences
from an analytical to a synthetic science and began the material development of
genetic engineering (Krimsky 1999). This began the shift to molecular genetics and
genomics that was aided by the use of computers to analyze sequence data
(Galambos and Sturchio 1998; Mackenzie 2003). Scientists could see and
conceptualize the human body in new ways and begin to map and manipulate the
very building blocks of life. These three different trajectories have been converging
in the last fifteen years and are productive of a new model for constructing racial
difference.
The relationship between identity and technology is transforming at a time
when a new understanding of the racial order is pervasive in society. Scholars began
referring to the change from overt racism of the Jim Crow fashion to coded and
covert forms of racial inequality as the new racism (Barker 1982). More recently, the
concepts of color blindness (Wellman 2003), “laissez faire” (Bobo et al 1997), and
color blind racism (Bonilla Silva 2001) have been used to characterize the cultural
and structural changes in the racial system since the 1970s. This dissertation extends
the arguments made by Bonilla Silva (2003), Gray (2005), and Collins (2005) and
suggests that the ideology of color blindness has converged with the changing
organization of society due to globalization, the emergence of the information

4
society, and the evolution and diffusion of ICTs. This convergence is productive of a
new form of racialization. Race is being re-constructed around codes that do not
necessarily depend on the epidermal body or culture, the traditional markers of racial
difference. Instead, the social construction of race is becoming a process where
information is the material by which social, economic, and political meaning is
worked on. Racial identity, meanings, and structures are being created in terms of
information collected, stored, analyzed, and distributed through the use and shaping
of communication technologies. I want to suggest that the new interaction between
culture and technology in the making of racial identity can be understood as the
informationalization of race.
Informational processes have been integrated across commercial,
governmental, and personal spheres of society. When society undergoes fundamental
changes, deep-seated aspects of its structure like race and racism require
examination. This is especially needed when the rhetoric of change, typical of the
information age, is so widely heralded as progress for all. Future studies need to
assess the ways in which traditional social institutions that have a history of
discrimination, such as insurance and law enforcement, have adapted to the current
historical context and identify new and emerging areas, such as online business. This
dissertation examines the specific mechanisms and manifestations of the
informationalization of race in the production of scientific and medical knowledge
and its applications through an analysis of the technological, institutional, and
cultural changes in human genomics.

5
Media scholars have argued since the 1970s that the media’s relationship to
power is not one of a reflective representation (Hall 1980; Morley 1980), but a
constitutive element of social institutions and cultural practices. Rather than being
accurate or inaccurate portrayals of the social world, the media plays an integral role
in the making of social life. In a similar vein, information and communication
technologies are not neutral conduits of scientific ‘discoveries’ and economic
practices. Their stories frame our perceptions and actions. ICTs are embedded in our
everyday lives and societal institutions and their contributions need to be understood
as directly contributing to how we know, organize, and act in the world. While
scientists argue over the accuracy or inaccuracy of scientific data, which is the
outcome of computational routines, we might back up and examine how those
outcomes stitch together cultural assumptions, molecular particles, microprocesssed
bits and bytes, and historical context. The informationalization of race is indicative
of larger developments in the formation of the information society where qualitative
and quantitative changes in the management, organization, and manipulation of
information have been taking place since the 1970s. This is in no small part to the
new technological paradigm that began in the 70s with the development of
communication technologies.
My task in this dissertation is not to determine the veracity of scientists’
assessment of the structure and function of the genome or whether or not ‘race’
exists at the molecular level. These sorts of assessments are not the domain of the
social researcher. This is similar to the media scholar who is not in the business of
assessing whether or not the images the media constructs are true or not true, but

6
how they constitute cultural frameworks for interpreting the social world, enabling
and constraining human action, and allocating society’s resources. Genomic science
is constructing representations of the human body, race, and the natural versus the
social. My aim is to explain how ideas about race and the genome are produced,
circulated, and (dis)agreed upon. Claims such as “We have the structure but no
meaning yet” (Lander in Marturano 2003:208) of the blueprint of life, the human
genome, do not take into account the cultural power of genetics. Even scientific
knowledge of the genome is a priori as scientists engage in framing the nature of
their work in the early stages. Knowledge about the significance of the human
genome pre-dated the first draft. The genome as an object of knowledge was
constituted in early discussions about what possibilities its ‘discovery’ could hold.
Whether by convincing the U.S. government to grant funds or attracting venture
capitalists to invest in Celera, the competing private firm in the Human Genome
Project, stories about the value of genome research had to be deployed. These stories
are imbued with cultural, scientific, and economic meaning. In US society, where the
gene has become a powerful (global) icon, genetic explanations for phenomena as
disparate as cancer and men’s proclivity to cheat on their partners have come to
occupy a primary position in the popular imagination as well as scientific facts (Lee
2003; Nelkin and Lindee 1995). The twentieth century has already begun to look like
the century of the genome.
In order to understand how the genome and race are being articulated we
need to examine the technological, regulatory, and discursive infrastructures that
enable and constrain genomics. Through the domain of science and the case of

7
genomics and the HapMap project, I argue that the informationalization of race is the
result of the convergence of these three elements: the role of technology in the re-
organization of biology, the legal and institutional development of human genomics
and biotechnology, and discursive formations of genomics and race. Taken together,
a new picture of racialization in this century emerges. At the heart of this shift is the
new technological paradigm that includes innovations in databases, data mining, and
the Internet, the industrialization of scientific research, and the dominant racial
paradigm, color blindness. The construction of difference is produced from the
shaping of new communication technologies in the network age.

Research Questions and Rationale
The dissertation examines the role of communication technologies in the
management and manipulation of information and how the process of sorting people
is productive of social identities and, specifically, new modes of racialization. The
technologist, policy, and market proposition has been that the information revolution
will liberate society from discrimination. Instead, this dissertation argues that there is
occurring a deepening of social practice in terms of the material and symbolic
construction of racial difference and racism. Increasingly, scholars have been urged
by the rapid and escalating development and innovation of new information and
communication technologies to examine how social processes and actors shape them
for the purposes of individual expression, community building, political
participation, social control, and resistance. By far, the technology literature tends to
address critical questions of social power along the lines of Jurgen Habermas (1962,

8
1989), Howard Rheingold (1994), and Robert Putnam (2000). While these authors
address issues of the public sphere, community, and civic participation their work,
which represents the mainstream of academic and public discussions, technology
scholars who follow their arguments tend to ignore how the social shaping and use of
communication technologies enables and constrains racial and gendered identity.
This is not to say that race is entirely absent. Racial equality in terms of access to the
Internet has been an important concern in discussions of the digital divide (Mack
2001; Servon 2002). Still, these perspectives seldom examine the impact of
technologies and ICTs in particular on the nature of a racialized social system and
the ways in which minority communities in turn shape its development, diffusion,
cultural meaning, and utilize technology in everyday and acts of resistance. When
Latino high school students walked out of their schools in Los Angeles during the
spring of 2006 in protest of anti-immigration laws, (almost) everyone was surprised
that they organized and coordinated across the city using MySpace.com.
There is an expanding body of literature on technology and cyberspace that
situates race as its object of inquiry (Gray 2005; Hammonds 1997; Ignacio 2005;
Jenkins 2002; Kolko et al 2000; Nakamura 2002). This emerging body of work tends
to follow a cultural and media studies approach which examines both theoretically
and empirically the construction of cultural meaning through representation and
signifying practices in the media and through the use and shaping of new media
technologies. Using this approach as a starting point, I ask the following research
questions regarding the relationship between the shaping of communication
technologies and racial identity. How is the management and manipulation of data in

9
the information society producing new formations of racial difference and re-
producing old notions of racial identity? What impacts have developments in new
information and communication technologies have on racialization? How is race
being constructed in terms of information, instead of the traditional markers of
phenotype, culture, and nation?
A key site where the relationship between technology and identity can be
observed is the biomedical sciences and the biotechnology industry. There are others,
such as the insurance industry, law enforcement, and marketing and consumer
profiling. The informationalization of racial identity represents new trends in the
manipulation and management of information that cut across sectors and is a broad
social process that emerges from the rise of the information society, developments in
information and communication technologies, and the shifting politics of racial
identity and social inequality. This dissertation will focus, however, on the specific
mechanisms, processes, and observations, and their meaning in genomic research
through a case study of what is being described as the next Human Genome Project,
the HapMap Project. There is wide agreement that there have been concurrent
biological and electronic revolutions since the 1970s (Capra 2002; McGuigan 1999).
Focusing on genomics in general and, in particular, the HapMap Project allows for
the extrapolation of a general trend from the political economy and cultural
frameworks that structure the convergence between these two revolutions. As
mentioned above, information and communication technologies are central tools for
scientific research into the genetic origins of disease and developing pharmaceutical
treatments. One of the most salient social categories for scientific research and the

10
most controversial is race. From the development of race drugs to the Human
Genome Project, across medical, scientific, and pharmaceutical journals, debates
have been raging on how to use race in scientific and medical research. The
following questions stem from the notion that knowledge production can be analyzed
through two important facets, political economic and socio-cultural processes. How
does genetic research in the biotechnology industry construct race in terms of
information, rather than biology, culture, or nation? What is the role of information
and communication technologies in genetic research? What have been the economic
and regulatory changes that have facilitated convergence between the electronic
revolution and the genetic revolution, especially in terms of industrial and academic
relations? How do particular industry structures and new technologies enable
scientific and medical research? How do different actors in the biotechnology
industry view the place of race in genetic research? What happens to race when
population identification moves from phenotype to genotype?
The HapMap Project is significant for a number of reasons. First, unlike the
Human Genome Project, it focuses on differences between racial groups. While there
are a number of population groups that could have been compared by HapMap, the
DNA samples were taken from people with ancestry from Europe, Asia, and Africa.
These three countries code for the so-called base races of humanity in traditional
taxonomy and social understandings of race. Second, the members of the HapMap
represent three developments in scientific research: collaboration between an
inclusion of an Ethical, Legal, and Social Implications group (ELSI) made up of a
consortium of scientists, social scientists, bioethicists, legal scholars, and

11
representatives from non-governmental organizations (NGOs) to set the guidelines
for overall ethical concerns and community engagement; the globalization of
scientific research in terms of cooperation between nation-states; collaboration
between entrepreneurs, academics, and public servants. Third, HapMap shows how
information and communication technologies are becoming central to knowledge
production in the information economy. Without the Internet, databases, and
datamining technologies, the international research sites would not be able to
communicate effectively and efficiently with one another and download samples to a
centralized data repository, as well as map and analyze the sequence data. It is
widely accepted that genomics, the comprehensive study of genes, would be
impossible without ICTs. The HapMap project is an important site to understand
how such technologies are being shaped for social and scientific uses and the
meanings they produce. Finally, there has been an increasing interaction between the
biological revolution and the information and electronic revolution. The HapMap
project is a current example of this convergence not only in terms of man-made
technology meeting biology to produce knowledge and products, the techno-sphere
and the bio-sphere, and computer scientists meeting biological scientists, but of the
adoption of theories and techniques of biological science from computer science. As
this dissertation will show, HapMap is an information science.

Methodology
I employ the concept of the informationalization of race to highlight how
information and communication technologies are being developed and utilized in the

12
sorting of racial difference. Theoretically, this dissertation brings together
scholarship on the information age, science and technology studies, and critical race.
Understanding the relationship between technology and identity means making links
between technological, institutional, and cultural change. This approach borrows
methodologically from Stephen Small’s (1999) concept of racialization and Stuart
Hall et al’s “circuit of culture” (1997). Stephen Small’s article “The contours of
racialization” attempts to conceptualize race as dynamic and relational process.
Small argues that race needs to be understood in terms of its historical transformation
in both cultural representation and institutional structures. Both the image and the
institution organization are central to understanding the reproduction and
contestation of social inequality.
Stuart Hall and a team of scholars authored a six-book series entitled Doing
Cultural Studies as a course reader at the Open University in the UK. Released in
1997, the series aims to study a cultural artifact from five perspectives in what they
referred to as a circuit of culture. The circuit of culture brings together a number of
different approaches that had been emerging out of British cultural studies in its
development over the preceding two to three decades. Each book focused on one of
the five points of culture: representation, identity, production, consumption, and
regulation. This approach also attempts to intervene in the internal debate within
cultural studies about the relationship between culture and political economy and the
debates between political economy and cultural studies about the merits of either
approach for the study of communication and media (Garnham 1995, Grossberg
1995, Mosco 1996, Murdock 1995). Political economy foregrounds the production of

13
media industries and messages while cultural studies usually begins with the point of
reception or decoding as an object of analysis. Admittedly, these are both gross
generalizations of both political economy and cultural studies, especially the latter.
Similar to Small, Hall et al offers a broad conceptualization of the study of culture
that incorporates both culture and economy. Also, the series is concerned with the
emergence of new technologies and how they are being produced and incorporated
into cultural practices.
There are many different kinds of sources this dissertation draws on,
including semi-structured interviews of HapMap participants and scientists working
in the biotechnology industry, policy documents, scientific and biomedical journal
articles, and documents from the HapMap Project, the National Human Genome
Research Institute, an the National Institutes of Health, as well as a host of Internet
documents and websites. Due to the multi-site nature of the HapMap project and the
renewed interest in race as a category for biomedical research across medicine and
science, data collection for this dissertation is a process of following the object
(Marcus 1995). Interviews follow the international locations of the project.
Anthropologist George Marcus suggests that when the “thing traced is within the
realm of discourses and modes of thought, then the circulation of signs, symbols, and
metaphors guides the design” of the research (Marcus 1995: 108). Thus, textual and
documentary analysis traces discussions about race, genomics, and health across
various journals and across different fields within the biomedical sciences. Also, I
pay particular attention to a select number of major journals in the fields of science,
medicine, and genomics.

14

Makeup of Interviewees
Interview research allows for an in-depth understanding of the issues involved in the
development and application of genomic technologies. Interviews are able to gain a
detailed description of the scientific process and the production of scientific
knowledge of race (e.g. the use of such categories and the creation of them), the role
of communication technologies in genomic research, and applications for such
knowledge. A total of 26 interviews with members of the HapMap Project were
conducted from May to October in 2005. Subjects were recruited by letter, followed
by Email, and then telephone. They included geneticists, lawyers, anthropologists,
bioethicists, doctors, bioinformaticians, project managers, biologists, directors of
NGOs, pharmacologists, and senior scientists for a leading biotechnology company.
This configuration of professions is increasingly common on international genome
projects and big science, in general. Since HapMap is a global project, interviewees
for this research were located in Canada, Japan, Thailand, the UK, and the United
States. Because of the high cost of travel almost all of the interviews were conducted
by telephone and ranged in time from 30 minutes to 1.5 hours. They were all
recorded on a digital format and transcribed.
The interviews were semi-structured containing three sections on technology,
university-industrial relations, and race. The initial research design focused on the
themes of technology, genomics, and race. However, my approach developed a
recursive relationship between the data and the research questions. The format
included both structured questions and allowed for conversation to emerge when

15
necessary. While each interview created new ways of looking at the questions, I
stayed with the original format as much as possible, augmenting and clarifying
questions as new information became available. Each interview was a process of
refinement as well as continuity. Even though questions were of a professional
nature and deemed low risk to the subjects, interviewees were guaranteed anonymity.
Since the topic of race and genomics has become a controversial issue, I hoped that
concealing their identity would allow these professionals to express their personal
views more readily. However, statements are not attributed to single individuals but
seen as representative of general themes. Descriptors of the individuals’ positions
were included to contexualize the responses without making them identifiable.

Makeup and Analysis of Textual Data
Primary textual data was collected from key biomedical and scientific journals such
as Science, Nature, Nature Genetics, Genomics, Genome Biology, the Journal of the
American Medical Association, and the New England Journal of Medicine. While the
focus of the textual analysis was on these particular journals, I traced important
themes across other journals according to discussion threads identified by authors
that travel across various journals in the domains of science, medicine,
biotechnology, and public health. The major journals have been identified in terms of
standing in the fields of science, genetics, genomics, and medicine and also impact.
“Impact” refers to the amount of citations that articles from a specified journal occur
in other journals. There are a number of ranking systems that conduct such
measurements across the social and natural sciences. Scientific journals are all

16
available online which makes for an efficient data collection process. Primarily, the
perspectives, editorials, and opinion articles in these journals are where the debates
about the role and nature of race take place in each of the above fields. Overall there
were two levels of reading the data (Mason 1996). The literal level consists of the
content and substance of the articles, the ‘face value’ of the literature. The
interpretive level consists of what the implicit norms and values of the discourses.
This version is a blend of what I observed and what exists in the secondary literature.

Chapter Overview
In Chapter Two, I trace the three areas of scholarship that this dissertation draws
from: critical race, information society, and science and technology studies. This
chapter highlights the current literature on racialization, technology, and science by
focusing on the relationships between race, the information society, and new
communication technologies. This chapter explores the new conditions and
mechanisms of racial structuring and representation in society. Central mechanisms
of change and continuity are innovations in new communication technologies, the
rise of the information age, and the dominance of a color-blind, racial ideology. To
this end, I suggest that a new formation of race is emerging called the
informationalization of race. Then I explain the theory of the informationalization of
race and situate it at the confluence of the three areas how is contributes to them. The
chapter ends by introducing the case study on which this dissertation is based,
genomics and the HapMap project, and how biology has become an information
science.

17
Chapter Three returns to the third process outlined in the introduction, the
biological revolution. Since the discovery of recombinant DNA in the early 1970s,
biology has grown from the public and academic labs to a burgeoning biotechnology
industry. This shift has largely been due to innovations in information and
communication technologies and the incorporation of computing technologies and
computing methods into the toolkit of biology. A new type of biology based on
computational techniques has emerged and joined the wet labs of experimental
biology. I describe how biology has incorporated theoretical and practical aspects of
computing to become an informational science. Genome projects such as the Human
Genome Project and the International HapMap Project have not only incorporated
these technological and scientific transformations, but also motivated them. Many
scholars acknowledge the central role of computing power and software in the rise of
genomics. I explore the role of two equally important technologies that serve more
than simply instrumental purposes in sequencing DNA, databases and the Internet.
Chapter Four turns from technological innovation to regulatory
transformations that organizes and shapes scientific, ethical, and legal process that
genome projects operate within. From 1977 to 2004, key legal and institutional
changes took place that enabled and constrained the biotechnology industry and
scientific research, both in and outside of academia. Since the Bayh-Dole Act of
1980, a series of government policies deregulated the relationships between
commerce and universities with the aim of making the US a world leader in
biotechnology. This produced a climate where some scientists became weary of
research submitted to journals that was increasingly being funded by corporate

18
interests. Journal editorials, comments, and letters to the editor become forums for
discussing conflict of interest policies. Finally, another set of institutional changes
also occurred in the same journals as well as government organizations such as the
National Institutes of Health that were aimed at the inclusion and treatment of ethnic
and racial minorities in biomedical and scientific research.
In the Chapter Five, I examine the role of cultural and scientific discourse in
genomics. While scientific research is built on a position of neutrality from the
object of study, it is impossible to study race without common sense understandings
of racial difference ‘infecting’ the purity of science. Scientific discourse is embedded
in cultural assumptions about the nature of race and social order. I argue that four
discursive frames characterize the informationalization of race and examine their
relationship to discursive formations of race, genomics, and health in scientific race
talk.

A Brief Introduction to the HapMap Project and Genomics
The HapMap Project is a $130 million venture to find variation in the human
genome that is linked to disease. The project was initiated by the National Human
Genome Research Institute (NHGRI) of the National Institutes of Health (NIH) and
began with a planning meeting held in Washington, D.C. in the summer of 2001. The
two-day conference featured panels and discussions about the scientific, ethical, and
legal issues involved in a study that would span four continents, link up major
research centers in the world, and draw on subjects from identified communities. The
project launched in 2002 and Phase I was completed in 2005. Phase II has been

19
underway and consists of further mapping and sequencing work being performed on
the original four populations. Sample collection of Phase III, expanding on the
original four population groups, is currently in progress. Even though it was billed as
the next Human Genome Project, it has not received the same type of fanfare and
media hype as the competing human genome projects, which jointly completed a
draft of the human genome in 2001. Where the HGP featured competition, HapMap
is a collaborative venture that spans academic disciplines and international borders
including participants from Canada, China, Nigeria, Japan, the United Kingdom, and
the United States. The press conference for the HGP took place at the White House,
announced by President Clinton with co-heads of the two groups, Craig Venter of
Celera and Francis Collins, and a satellite linkup with British Prime Minister Tony
Blair in London. Recently, Phase I of the project was completed, culminating with an
online database that is open to anyone who wants to make use of the data. The
completion of the first phase was marked by publication of the findings in the journal
Nature (International HapMap Consortium 2005) and a simultaneous press
conference featuring NIH Director Francis Collins and the various chairs of the
project’s sub-committees.
1
Only Collins led the proceedings, without President
George Bush. While it was hardly a media event, the impact of HapMap findings for
racial identity could prove to be far more profound than the Human Genome
Project’s findings that humanity is 99.9 percent the same at the genetic level.
The International HapMap Project is described by organizers as the next step
in understanding genetic differences in human populations. Through the information

1
The press conference can be streamed at http://www.genome.gov/17015416.

20
gained in this project, scientists hope to determine the “common patterns of DNA
sequence variation in the human genome” which will “allow the discovery of
sequence variants that affect common disease, will facilitate development of
diagnostic tools, and will enhance our ability to choose targets for therapeutic
intervention” (Ibid; see also Deloukas 2004; Lee 2003; Rotimi 2004). The change to
cataloguing haplotype blocks and linkage disequilibrium in the search for ancestral
differences is a key development in genetics in the last five years that has been made
possible by advances in computing. Scholars argue and initial interviewees from the
HapMap project unanimously agree that there could be no new genetics without
computing science. One of the most significant challenges in the Human Genome
Project was the computational analysis of massive sets of data. Developments in
communication technologies have made possible genetic technologies faster and
cheaper, such as high through put, and data sharing through the Internet. In this
regard, HapMap has built on the technological innovations of the Human Genome
Project. HapMap has also built on the social failings of the Human Genome
Diversity Project (HGDP).
Led by renowned geneticist Luca Cavalli-Sforza, the HGDP sought to map
differences between human population groups globally. However, the HGDP works
on the old model of population groups from nineteenth century notion of populations
(Reardon 2005). The U.S. model has shifted to admixture of populations, which
includes North American genetic technologies such as ancestry-informative markers
(AIMs), which give clues to one’s ethnic background. HapMap raises key issues in
regards to methodology in academic and commercial research into human

21
populations. What are the techniques used by academics and pharmaceutical
researchers in identifying the populations before they conduct the research and what
is the rationale? Usually the debates about the history of anthropological genetics are
cited as the justification for choosing one possible sample population over another.
This was certainly the justification used for the for the HapMap project. Interviews
with the members of HapMap concerning the creation of the project show how their
discussions built on the issues of sampling and community politics from the Human
Genome Diversity Project. While the scientists heading the HGDP were well
intentioned in their pursuit to explore the diversity of the human species, their efforts
were met with controversy and opposition from many groups, especially indigenous
organizations from around the world. In her seminal history of the project, Jenny
Reardon (2005) asks how the scientists working on the project who have been
advocates and leaders of efforts against the legacy of scientific racism could be
charged with racism and their effort dubbed the Vampire Project. Apparently the
scientists involved were unaware of the socio-political context in which their work
would be situated. The members of the HapMap Project are not able to claim such
ignorance. HapMap is a revision of the early failings of the Human Genome
Diversity Project (HGDP). One of the outstanding issues is the choice of two Asian
populations and only one African one. The rationale runs contrary to what is known
about anthropological genetics.
The HapMap consortium collected samples from “populations with ancestry
from parts of Africa, Asia, and Europe” (International HapMap Consortium
2003:789). While project organizers deliberately decided to refer to the sample

22
groups in terms of populations and not racial groups, the initial groups do match a
traditional American taxonomy of race. When the National Human Genome
Research Institute (NHGRI) of the National Institutes of Health (NIH) decided to
build databases and a haplotype map the scientists involved decided that the groups
would be labeled according to geographical rather than racial signifiers: CEU for the
population from Utah, CHB from Beijing, China, JPT from Tokyo Japan, and YRI
for the Yoruba from Nigeria.
Beginning with the initial planning meeting held in Washington, D.C., in July
2001, the planning discussions focused on ethical issues in terms of group
participation and the implications of the project for race and ethnicity. Attendees
included social scientists, bioethicists, legal scholars, as well as representatives from
Native American groups and the NGO, Indigenous Peoples Council on
Biocolonialism. Sociologist Troy Duster, a leading critic of the social implications of
new genetics on health and law enforcement, led one of the sessions entitled,
“Ethical and social issues relating to the inclusion of identified populations in a
haplotype map project.” He discussed the issues of inclusion and exclusion of certain
groups, eliminating versus retaining ethnic group names to describe the data, and the
risks of the project conveying meaning to race/ethnicity (HapMap). A session on the
ethical and social issues of consent and inclusion of identifiable populations by a
panel of experts followed. Learning from the challenges of the HGDP, members of
HapMap seemed well aware from the beginning of the socio-political world in which
their efforts would be situated.

23
But while largely scorning conventional racial categories, population
geneticists and researchers equipped with new genotyping tools are
increasingly identifying patterns of genetic variants, particularly single-
nucleotide polymorphisms (SNPs), that are prevalent among specific
populations. Researchers have found that SNPs, variations of a single
nucleotide at a particular spot on a chromosome, tend to occur in blocks
called haplotypes. (Rotman 2005)

The fact that racial and ethnic categories were on the table in a serious
manner is largely the result of the public nature of the NIH, the structure of the
funding for the Human Genome Project, and advocacy groups protesting the Human
Genome Diversity Project. Since the HGP was a federal project, (“biological and
social scientists, health care professionals, historians, legal scholars, and others”)
bioethicists and social scientists, pushed for the creation of a sub-group to be
primarily focused on ethical issues. The result was the inception of the Ethical,
Legal, and Social Implications program (ELSI) as an institutionalized subsection of
the NIH’s National Human Genome Research Institute (NHGRI), headed by Francis
Collins, the director of the public Human Genome Project. ELSI receives 5 percent
of the total federal funding allocated to the NHGRI. It was this type of funding and
feedback from scholars and professionals outside of the scientific community that
stopped the HGP short of going all out on race. In terms of the HapMap project,
ELSI members were crucial in ensuring that the markers included in the HapMap
database were not racially coded, but, rather, were sorted in terms of the population
group’s geographical location (Interview 1013; www.hapmap.org). Differences in
responses to this issue by scientists and non-scientists clearly show how the naming
originated from ELSI members. While the scientists and bioethicists seem to agree
on trying to avoid the discrimination and racism of the past in regards to science and

24
race, some respondents clearly viewed ELSI’s insistence on the specifics of language
used to code the population groups, such as CEPH for the sample from Utah and JPT
for the sample from Japan, as simply semantics. There was the sense that issues such
as the type of language used up time in meetings that could have been better spent on
other, more scientific or technical, matters. One of the most important contributions
of ELSI is putting race on the table for discussion and the implementation of
protocols for community engagement and consent. While the ethical parameters of
scientific research have previously been the domain of scientists, in recent years
social scientists, legal scholars, and bioethicists have been included in scientific
research.

25
Chapter 2
The Informationalization of Race:
Communication technologies and the New Conditions of Racialization

Race, Racialization, and Colorblindness
Social constructionists of race have delineated the nature of race into two, main
ontological phases, which overlap and are interdependent of one another: race as
biology and race as culture. In both paradigms, racial identity is constructed by
locating social hierarchy in bio-cultural markers of group identity and behavior. In
the race as biology paradigm, racial groups are identified according to a collection of
physical phenotypes, such as skin colour, hair texture, shape of eyes and nose
(Banton 1998; Jordan 1974; Miles 1989). These epidermal markers have been
socially understood to indicate biologically determined group characteristics and
have also set the political limits of inclusion to national citizenship and access to
material resources. Scholars have located the origins of the biologizing of race in the
nineteenth century by scientists and population geneticists, such as Francis Galton,
working from Darwin’s evolutionary model (Foucault 1978; Gould 1996).
In the race as culture paradigm, racial difference is constructed from cultural
material (language, practices, and behaviors), often understood as ethnicity, rather
than the body. This development has been referred to as the culturalization of race
(Razack 1998) or the new racism (Barker 1982), which signifies the shift in
discourse from biological classification to cultural codes, yet the practice of racism
remains quite similar:

26
In its modern form, overt racism, which rests on the notion of biologically
based inferiority, coexists with a more covert practice of domination encoded
in the assumption of cultural or acquired inferiority. (Razack 1998:60)
Group characteristics continue to be ascribed based on the symbolics of the body, yet
the real differences between groups are no longer biological (although there are still
some social scientists who pursue this belief) but rather ethnic or cultural. Racial
signification and positioning in public discourse is rearticulated in cultural terms,
“talk about a group’s culture often serves to disguise what are fundamentally racial
claims” (Kim 1999:117). A minority group may not be biologically inferior,
however, they are set apart through their perceived ‘difference’ from the dominant,
white culture. Not better or worse, but different. Still, the Other is juxtaposed and left
on the margins of social inclusion. Against conservative narratives of racial progress,
Kim argues that the move to culturally coded racial discourse has in fact stabilized
White privilege in the post-civil rights era.
It is precisely because it has been revamped in nonracial language that the
field of racial positions functions so effectively to reinforce White privilege
today. Representing a cultural explanation for group inequalities, the field of
racial positions implies that American society is substantially colorblind and
that the American Dream is still viable. (Kim 1999:117)

Bio-race does not seem to be present in current public discourse about group
differences, however culture is deployed in a similar manner, homogenous,
dislocated from history, and static, and containing an implicit association to group
position in the social order: “a fixed property of social groups, [rather than]
“something intrinsically fluid, changing, unstable, and dynamic” (Gilroy 2000:266).
White supervisors and co-workers in an the context of the workplace may explain the

27
behavior of a minority person who is facing racism as a “cultural thing.” What the
former group may interpret as a ‘bad attitude’ could simply be a person of color
standing up for himself in the face of individual and institutional discrimination.
Scholars of race have investigated the role of institutions in re-producing race
and racism such as the law (Crenshaw 1993, 1995; Crenshaw and Peller 1993;
Williams 1991), policing (Chan and Mirchandani 2002; Hall, Critcher, Jefferson,
Clarke, and Roberts 1978; Holdaway 1996), education (Lopez 2002; Razack 1998),
and the media (Cottle 2000; Dyer 1997; Entman and Rojecki 2000; Gray 2000; Hall
1980, 1981, 1992; Hunt 1997, 1999). The turn to popular culture, especially in
cultural studies, has been productive for understanding how racialization is
negotiated in everyday life (Fiske 1996; Hall 1996; Hooks 1992; Spigel 2001).
Recently, there has been a call to understand the role of whiteness (Morrison 1992).
Scholars have responded by tracing the historical process of European ethnic groups
becoming white (Ignatiev 1995; Jacobson 1998), the relationship between race and
class (Roediger 1999), and how popular cultural forms, such as black face
minstrelsy, were sites of constructing racial identification through difference (Lott
1993; Rogin 1996). The above scholarship widely covers how racial difference is
constructed and operates to re-produce social inequality. The next section explores
major theories of race that largely underpin the above domain studies.

From Race Relations to Racial Projects and Racialization
One of the most influential and widely cited theories of the process of racial
construction is Omi and Winant’s (1994) theory of racial formation process. They

28
argue for an anti-essentialist conceptualization of race and offer a theory for studying
the meaning of race and racism. They define racial formation as "the sociohistorical
process by which racial categories are created, inhabited, transformed, and
destroyed" (55). Their theory situates race as "an unstable and "decentered" complex
of social meanings constantly being transformed by political struggle" (Ibid). Central
to their analysis are two ontological conditions that the authors recognize in their
theory. The first condition is the primacy they give to race as a social phenomenon
and the second concerns the relationship between cultural representation and social
structure.
Omi and Winant argue that race is not reducible to other social phenomena,
such as class or market forces. While race operates in articulation with other forms of
difference, such as gender and sexuality, its formation does not lie at the intersection
of other, primary social processes and institutions. Race is an autonomous aspect of
society rather than an anomaly within it or an epiphenomenon. A theoretical position
that recognizes race as a social phenomenon rather than an epiphenomenal one seeks
to,
…avoid both the utopian framework which sees race as an illusion we can
somehow "get beyond," and also the essentialist formulation which sees race
as something objective and fixed, a biological datum. Thus we should think
of race as an element of social structure rather than as an irregularity within
it; we should see race as a dimension of human representation rather than an
illusion. (55)

Often the manifestations of race are studied in institutional settings, such as the law,
education, and labor (Smith 1995) or in terms of its cultural representations, such as
in the media (Collins 2005; Gray 2005; Hall 1992). Race is understood as part of the

29
organization of society into different strata, or subgroups, or as identities mapped
and inscribed onto or representing different types of human bodies. Structural
analyses measure levels of racial discrimination in institutional settings, but are
unable to describe how differences forms of racial difference emerge, diffuse, and
transform through symbols and cultural practices. On the other hand, approaches that
treat racism as systems of signification cannot make sense of disparities in health or
enrolments of visible minorities in higher education. Racial formation process
acknowledges the importance of both methods of analysis. Race is a case of both
structuring and signifying where the two inform and construct one another. We
create our social structures out of the way we understand the world and our actions
are enabled and constrained by our social structures. The ontological perspectives of
the primacy of race and the link between social structure and cultural representation
lay the groundwork for Omi and Winant's theoretical approach, racial formation
process.
The authors elaborate on racial formation process in two ways. Firstly, racial
formation is "a process of historically situated projects in which human bodies and
social structures are represented and organized" (Ibid). Secondly, racial formation is
linked to the evolution of hegemony. Here, hegemony is understood as the manner in
which society is organized and ruled through consent rather than coercion. The
authors believe that a whole scope of social problems involving race, such as sexism
and other forms of difference, inequality, and oppression and their relationship to
race, can be understood through their approach.

30
Ideologically, racial projects do the work of making the links between social
structure and cultural representation.
A racial project is simultaneously an interpretation, representation, or
explanation of racial dynamics, and an effort to reorganize and redistribute
resources along particular racial lines. Racial projects connect what race
means in a particular discursive practice and the ways in which both social
structures and everyday experiences are racially organized [emphasis in
original] based upon that meaning. (56)

The idea of racial projects helps to broaden the scope of race and racism and the
question of rule. Instead of restricting the conceptualization of inequality to the
domination of one group over another, racial projects spread the struggle of rule over
a network of power relations. Projects act as the "building blocks not just of racial
formation, but of hegemony in general" (68).
Racial formation has been taken up by a number of scholars of whiteness (see
Frankenberg 1993; Jacobson 1998; Lipsitz 1998; Roediger 1999). In Whiteness of a
Different Color (1998), Jacobson explains how different European groups became
white through three great racial projects of U.S. immigration history. Entrance into
whiteness was intimately bound to changing notions of citizenship, naturalization
laws, and white ethnic consolidation. Roediger (1999), in his study of nineteenth
century working class culture, shows how racial formation and class formation were
“bound to penetrate each other at every turn,” but not reduced to one another, and
that “the pleasures of whiteness could function as a ‘wage’ of white workers” (20, 8,
13). Similarly, Lisa Lowe (1996) links class formation to racial formation, as well as
gender formation, in the history of Asian immigrants and Asian Americans. Like
Jacobson, she argues that a key site is the law and the construction of citizenship.

31
The law and relations of production have a recursive relationship as the former “must
be understood as both an ideological and a repressive state apparatus, as both
symptomatic and determining of the relations of production” (14). Lowe builds on
the relationship between structure and agency in racial formation by showing how
the law worked both discursively and institutionally. For Lowe, the process of
racialization was intimately tied to class and gender formation of Chinese laborers,
the state legal apparatus, and the wages of whiteness.
Racialization is another widely used concept used to analyze race. This term
is often discussed in place of or in conjunction with racial formation. Similar to
racial formation, racialization argues that race is not a category, but a process imbued
with power. Part of the problem with dealing with race or race relations is getting
away from the idea that race is something tangible, static, natural. A race relations
paradigm continues to force the terms of discussion and analysis into the framework
of a pre-existing phenomena; there are biological races and they engage (or do not)
in relations with one another and racial conflict is the outcome of races (or ethnic
groups) in contact (Small 1999:48). Stuart Hall reminds us of the deconstructionist
position, which he refers to as a post-modern mantra: “race is indeed a
sociohistorical concept, not a transhistorical discourse grounded in biology” (Hall
1998:190). Racial formation makes a progressive analytical step towards thinking
race as a social, cultural, political and economic operation that is contingent on place
and time, geography and history. Robert Miles (1989) uses racialization to refer to
those,

32
instances where social relations between people have been structured by the
signification of human biological characteristics in such a way as to define
and construct differentiated social collectivities. The characteristics signified
vary historically and, although they have usually been visible somatic
features, other non-visible (alleged and real) biological features have also
been signified. The concept therefore refers to a process of catagorisation, a
representational process of defining Other (usually, but not exclusively)
somatically. (75)

The process of signification in racialization is a dialectical one where the definition
of the Other involves the definition of the Self. Miles uses racialization to refer to
instances where the discourse of race is linked to biological traits, which is largely a
process of signification. Where his analysis falls short though is in linking ideology
to social structures and cultural practices or forms of racism that are not dependent
on typologies of race (Anthias 1992:11). Small (1999) makes this correction through
a reconceptualization of race and race relations in terms of the process of
racialization in a manner akin to Omi and Winant’s racial formation.
Like Miles, Small’s definition breaks with static and biological notions of
race. However, Small favors a social, contextual, and relational concept that forces
an interrogation of the everyday practices of people, the signifying of social
meaning, and institutional practices. In short, he proposes an investigation of
“economics, politics, power; and to the ways in which structures, images and
ideologies operate to sustain inequality and injustice” (Small, 1999:49; see also
Cottle 1992:4). Similar to racial formation, racialization enables the tracing of the
emergence and dynamic nature of the concept and action of race in social institutions
and cultural representation: in the law, in movies, in slavery, in politics, in the work
place, in schools, in sexuality, in gender, and in cyberspace, for example.

33

Beyond Black and White: Racial triangulation
In recent years, there has been a call for discussions of race to go beyond the black-
white binary that has defined racial discourse in American history. Many scholars
have found inquiries into the nature of race and society solely from the experiences
of whites and blacks to be limiting for social analysis. This leaves out a myriad of
other relations in American history, such as the experiences of Asians, or Latinos
have not been pursued. In a post-Civil Rights era that is increasingly being defined
by the presence of immigration from the global south a bipolar lens is becoming
increasingly limited. This hides many of the complexities of racial formation in
America in terms of the relationship between the dominant and minority populations
as well as between minority groups. Omi and Winant’s racial formation and Smalls’
racialization framework have both been attempts to move beyond the black/white
binary. Claire Kim critiques racial formation as a “different trajectories approach”
(1999:105). While racial formation is an open-ended, variable process that focuses
on the different histories of Latinos, Native Americans, Asians, and other groups,
and formations of racism, it tends to situate each group in isolation from one another.
Each group’s experience with the racialization process is not simply in binary
relation to the dominant culture, but in relation to each other.
Kim seeks to foreground the intimate connections between the different
groups and the relational nature of group identities through racial triangulation. Kim
(1999) argues that minorities are set in triangulation with one another and the
dominant white group. For example, Kawai (2005) argues that Asian identity has

34
historically been located between the dialectic of the yellow peril and model
minority. Employing racial triangulation, Kawai explains how white constructs of
Asians and African Americans are related to one another in representation and in
history. Kawai reveals where the term the model minority may have first entered
public discourse. Two separate articles in the mainstream media have been credited
with the emergence of the model minority myth, one in the beginning the 1966 in the
New York Times and the other at the end the same year in U.S. World News and
Report. Both described Chinese and Japanese groups as exemplary minority success
stories (Kawai 2005:113). The implicit assumption in creating a model minority is
that other minorities are not working hard without assistance to pull themselves up
by their own bootstraps. Both articles defined African Americans as a problem
minority. It was no accident that the two articles constructing the model minority
were published in the middle of the 1960s when African Americans were working
hard through organization and protest to progress their place in American society. Of
course, to mainstream media outlets such as the Times and World Report, protest is
viewed as complaint and African Americans are looking for a hand out. While Asian
Americans are located above African Americans in terms of their perceived work
ethic, they are triangulated and subordinated to Whites as an unassimilable Other in
American society. Often, especially in media texts, there is little distinction between
Asians and Asian Americans (Kawai 2005).

35
The New Racial Order: Colorblindness
Racial triangulation operates through the post-civil rights claim that America has
moved from a society structured in racial dominance to a colorblind society. The
color-blind racial structure attributes differential standings of minorities to market
forces, naturally occurring phenomena, and cultural variations between groups
(Bonilla-Silva 2003:2). In his racialized social system framework, Bonilla-Silva
argues that race and the process of racialization are structural parts of society and the
rational actions of individuals, not simply reducible to individual attitudes and
irrational behavior. Bonilla-Silva’s framework draws on Omi and Winant’s theory of
racial formation (1994). Like Bonilla-Silva, they argue that race and the conditions
of racism are historically contingent and depend on social, political, and economic
context.
Bonilla-Silva (2001) suggests that the civil rights movement marked a shift in
the racial structure of society from Jim Crow racism to color-blind racism. In the
former phase, blacks and other minorities were considered inferior to whites because
of their biological and moral inferiority. Where biology and social construction
demarcated a fairly clear line between proponents of racial ideology and progressive
politics, the former became much more out of fashion after the civil rights
movement. In the aftermath of social protests from 1955-1965, overt forms of racism
became much less socially acceptable and the structures of society such as the law,
economics, and higher education became the next sites of struggle. The
advancements of the 1960s and the feelings of social and political progress that

36
culminated in the Voting Rights Act of 1965 were sharply contrasted with the
economic problems of the 1970s.
There has been a political and ideological flip in the politics of the civil rights
era in the US. Concepts of social and political equality where everyone is created and
treated equally was based on the notion of sameness, of a common humanity.
However, one of the routes to equality for African-Americans and other minority
groups was the enactment of color-conscious policies, such as school busing and
affirmative action. Most people on the left or right would not espouse racial
ideologies of superiority and inferiority based on the assumption of biological
difference, at least, in an overt manner.
African American politics of the post-civil rights era sees to be between a
rock and a hard place. Racial segregation as the legal mechanism for racial
oppression has been struck down and the racial ideologies that justified it
have been forcefully challenged. Few would offer biological explanations for
African American joblessness, poor school performance, higher rates of
pregnancy out of wedlock, and higher rates of incarceration. But the changing
legal climate and the muting of racial theories rooted in biology neither
means that new forms of racism are absent nor that cultural arguments are
replacing biology as the reason given for African American disadvantage.
(Collins 2004:45)

As overt forms of racism and racial intolerance became socially unacceptable in the
1980s, at least in everyday public discourse, seeing and acknowledging race was
deemed a negative action. Many white people adopted a liberal racial consciousness,
rooted in making race invisible. One of the discursive techniques of whiteness
became, “I don’t see people’s color of skin. I treat everyone the same.” According to
this ideology, the denial of racial identity will itself produce an absence of racism.

37
However, four hundred years of racial domination and colonization of the body, in
representation, material practices, legal policies, government regulation, and policing
cannot and has not been undone in a couple of decades.
In the context of a colorblind society, diversity is celebrated along with
color-neutral policies. The language and practice of exclusion has been replaced by
the discourse of inclusion without considerations to “special interests.” For example,
college admissions that once systematically discriminated against women and racial
minorities now openly encourage such applicants. However, the color-conscious
measures that were products of the Civil Rights Movement to ensure diversity of
access to education and the workplace have been challenged across the U.S.
Affirmative action programs have been struck down in some states, notably in the
University of California system, through Proposition 209 in 1996. The politics that
lead the Civil Rights Movement, equality through sameness and color-conscious
policies, now are being seen as regressive. Proponents of a color-blind society argue
that color-conscious policies have outlived their usage and are now responsible for
reverse discrimination. In the 2003 gubernatorial elections, Proposition 54 sought to
bar any government agency or government funded organization from categorizing
people or collecting any statistics on the basis of race or skin color. Originating from
the Racial Privacy Initiative, conservative UC Regent Ward Connerly attempted to
continue from his success with Proposition 209, where affirmative-action policies
were struck down in the University of California system. The color-blind racial
structure attributes differential standings of minorities to market forces, naturally

38
occurring phenomena, and cultural variations between groups (Bonilla-Silva
2003:2). The new language of race is couched in terms of neutral codes that do not
directly reference the overt racist terms of the past.
Kim (1999, 2000) and others argue that the claim of race neutrality is
fundamentally ideological, as minorities still remain in subordinate positions in the
racial structure in terms of material conditions and civic participation. Against the
claims of racial uplift (Wilson 1987), racism continues to structure society
institutionally and in people’s everyday experiences. Smith (1995) takes on claims
such as William Julius Wilson’s that racism is declining in significance and any
disparities between racial groups are the result of historical racisms and,
increasingly, class. Through an array of empirical sources, including legal cases and
fourty years of survey data, Smith demonstrates how racism continues to persist in
American society at the micro and macro levels of everyday life and institutions and
across the domains of health, housing, education, consumer services, and
employment. More recently, Hurricane Katrina uncovered much more than dirt and
soil. When the storm was over, the aftermath of displaced people whose homes has
been destroyed revealed the deep legacy of slavery, the remnants of Jim Crow, and
the unfinished work of the second reconstruction. The news and Internet media fed
images of structural poverty and social exclusion straight into people’s homes around
the globe. The sheer coverage of African Americans in plight may be unmatched
since the civil rights movement.
2

2
Evidence of media bias was also on display on Yahoo.com. Now known as the
“two photo controversy,”
2
captions beneath a pair of photographs of people wading

39

Central Frames of Colorblindness
Bonilla-Silva (2003) identifies a number of central discursive frames of color
blindness. He argues that Jim Crow racism is characterized by ideological
assumptions about biology and morals while the new racism constructs different in
terms of culture and markets. Through an analysis of two sets of survey and
interview data from a Midwest college and Detroit, he finds four frames that white
respondents used when talking about race. Bonilla-Silva refers to them as abstract
liberalism, naturalization, cultural racism, and minimization of racism (27-30).
Abstract liberalism explains racial matters by using discourse associated with
political and economic liberalism. Race talk is relegated to abstractions about social
policies being achieved without being forced and people’s decisions should be left
up to individual choice. Whites who used a naturalization frame tended to explain
away racial situations by seeing them as natural and almost biological. For example,
this position would suggest that when minorities cluster together or whites choose to

through flood water with food described the African Americans in one of the photos
as looters and Whites in the other as simply as “finding” food. For discussions about
the “two photo controversy” and the photos themselves go to Media Awareness
Network and Snopes.com. The latter website includes quotes from the news
agencies, AP and AFP, and the photographers, Dave Martin and Chris Graythen
about what they witnessed before taking the pictures. Both photographers state their
choices of captions as facts, what they saw. Hall (1980) suggests that it is not due to
individual racism that the media operates in a manner that reproduces racial
stereotypes. Removing individual racists would change the practices and conventions
of news reporting and photography no more than removing individual stockbrokers
from Wall Street would disrupt capitalism. Regardless of whether or not the
photographers believe looting was taking place does not change the impact of
Yahoo’s posting of the two pictures. Yahoo’s editorial choice reveals the uncritical
manner in which human suffering can be framed in racialized terms.

40
date or associate with other whites is natural. People just like to be with their own.
Racial associations are seen as nonracial because they are grounded in some
instinctive urge for sameness. Bonilla-Silva’s conception of cultural racism is
consistent with the above discussions of the culturalization of race. Seeming patterns
among groups have become cultural rather than biological, such as differing
emphases on education and educational attainment according to race.
The minimization of racism fosters the belief that prejudice only exists if you
look for it. Those who point out social inequality whether through observation (no
one ‘experiences’ macro social processes such as the digital divide) or lived
experience, the micro level at which most public discourse on race takes place.
Black.White is a television reality show that examines racism purely at this level. The
show aired on Fox’s FX Network in early 2006 and was billed as an “experiment”
where two families, one black and one white, “switched” races through makeup and
prosthetics. They lived together in a house in Los Angeles’ San Fernando Valley for
a couple of months and shared experiences of each others’ “new” race. One of the
show’s characters is Bruno, the white father. When he becomes black, he has trouble
seeing racism at all, even when he and his wife go to a country and western bar and
are the only black people, or any people of color for that matter, in the bar. In one
scene, Carmen, his wife, is asked for a credit card by the bartender to finish making a
cup of coffee while she goes to ask Bruno a question in another part of the bar.
Carmen relates her experience to Bruno on the the ride home from the bar and
expresses surprise at the bartender’s request for a deposit and the obvious sense of
distrust. Bruno does not believe that his experiences as a black man are any different

41
than his as being white. The other father, Brian, becomes increasingly annoyed at
Bruno’s lack of acknowledgement of racism. Bruno responds to Brian’s ascertain
that he only sees what he wants to see by saying, “And you don’t see what you don’t
want to see.” The implication being that Brian frames out colorblindness from his
life perspective. Kim (2006) refers to Bruno as a “racism-denier.” Bruno does not
deny that racism exists, only that it does not exist in his life world. I would call this
the third-person effect of racism.
The third-person effect (Davison 1983; Perloff 2002) refers to th e notion
that the media does have an impact on individual behavior, but not to oneself. The
third-person effect “is an individual’s perception that a message will exert a stronger
impact on others than on the self” (Perloff 2002:490). A message has less effect on
me or you than on them. In terms of the every day lived experience of race, Bruno
has trouble seeing what Brian sees. This is more in line with Bonilla-Silva’s
minimization of racism, a central frame of colorblindness. For Bruno, racism is not
the central factor in his experience as a black man and he states that it is for Brian.
Like many whites who embrace colorblindness, it is not social inequality that
reproduces race and racism, but minorities who focus on it. Racism is something of
the past and not part of the present social structure. The third-person effect of racism
is based on the premise that individual choice and perspective is more powerful than
macro social forces and that one’s outlook, and the aggregation of different
individuals’ outlooks, can shape that phenomenon. An online response to Kim’s
article on the show on the journal Flow’s website is a typical framing,

42
The black family seemed to have a chip on their shoulders. I had to agree
with Bruno about people being treated according to their attitude they give
off. If you always think that everything negative that happens to you is
because [sic] of your race, guess what? you'll alway's [sic] be finding things
you can turn into racism. I know there is racism out there, but what people
need to understand is it's becoming less and less all the time. And if they
would not pass their negative beliefs on to their children, eventually it would
just about dissapear [sic]. But we have to let go and not alway's [sic] think
every thing white people do is done in malice. Every time they dont [sic] get
waited on rite [sic] away at a store, ect [sic]. Guess what? Im [sic] white, and
sometimes I don’t [sic] feel like I get treated fair at all. Let's all grow up and
quite playing the worn out race card.

Similar to Bruno, this respondent does not deny that racism exists; such a position
would seem irrational in light of what the popular imagination understands about the
history of the United States. However, the white power structure is not implicated in
this statement at all. One could assume from this comment that well-meaning whites
are the ones who are working to diminish racial inequality and it is minorities that
continue to perpetuate it because of the “chip on their shoulders.” The focus here is
on the individual and racism as an individual attitude, rather than as the result of the
racial structure of society. Brown et al (2003) argue that writers such as Dinesh
D’Souza (1995) and Shelby Steele (1999) perpetuate colorblind ideology by
claiming that progress has been made since the 1950s in addressing racial justice and
that racism is a thing of the past. Contemporary inequalities are not due to white
racism, but inactivity on the part of minorities, and that minority leaders keep racial
fervor alive so they can benefit from government programs (Brown et al 2003:6-7).
They do not suggest that racial discrimination has disappeared, but that discourse
about race should.

43
The Information Age and New Media Technologies
Contemporary discussions of information largely begin with Shannon and Weaver’s
(1949) theory of information as an orienting point and an important convergence
between computing science and telecommunications that would have a critical
impact on culture and commerce (Lyon 2005). Since then information has become a
ubiquitous symbol for changes in the structure of advanced capitalist societies. It
marks the rise of the information age which has its beginnings in the 1960s and
1970s and has been fueled by social movements, such as the civil rights and feminist
movements, the restructuring of capitalism, and the new technological paradigm,
such as the innovation and development of new communication technologies. The
literature on the information society is varied and voluminous. Webster (1999)
usefully separates scholars into those who endorse social change and those who
emphasize continuity. The former consists of theories of postindustrialism,
postmodernism, flexible specialization, the control revolution, and the informational
mode of development. According to Webster, these theorists, such as Daniel Bell,
Jean Baudrillard, and James Beniger, the old, modern, industrial society has given
way to a new one. Neo-Marxism, regulation theory, flexible accumulation, national
state and violence, and the public sphere characterize those theorists who focus on
the continuities in society, such as Herbert Schiller, David Harvey, Anthony
Giddens, and Jurgen Habermas, usually in terms of the re-production of the social
order (Webster 1999:139). While the two camps, “information society theorists” and
“informatization thinkers” (Ibid.), differ in their perspectives on the nature of society
and, within the camps, can be oppositional in the information age’s central

44
characteristics and mechanisms, they both acknowledge that information has a
special place in late capitalist societies that requires close attention.
Only during the twentieth century did information become central to the
social, political and economic organization of life and only late in that
century did information become inextricably linked with technology…In
today’s globalizing world the newer sense of information as coded,
commodified and computer-compatible is in the ascendancy. (Lyon
2005:223)

The two camps have differing perspective on change and continuity. This
dissertation will consider both the changes in technological and informational
infrastructures as well as the continuities in how technologies are deployed in a racial
system.
While minority groups fought to build on the gains of the civil rights
movement in the classrooms, offices, and studio back lots of 1970s America,
entrepreneurs were developing new innovations in computer technology in the labs
of Silicon Valley that would provide the technological infrastructure for the
information age. It is no stretch to say that a number of key discoveries together
ushered in a new technological paradigm. The new technological paradigm was not
the result of a social context such as the restructuring of capitalism or the arms race
in the 1970s (Schiller 1999). Rather, it was the result of a number of technological
discoveries that were independent but fed back and built on one another. The key
technologies were the microprocessor, the microcomputer, telecommunications, and
software, each clustering with another. Advances in each technology made possible
further advances in others. Thus, the new paradigm was not initially produced by
sociological design, but by a number of technological innovations and inventions.

45
When the new systems came into existence, however, their shaping and diffusion
was dependent on the cultural, political, and economic historical context. Put another
way, the development of technologies is never separated from the social realm. For
example, Webster (1999) argues that “research and development decisions express
priorities, and these value judgments, particular types of technologies are produced”
(143). Social values are always imprinted on technologies, such as bridges in New
York being designed at a certain height so that buses traveling from poor and black
neighborhoods are prevented from going to the beaches (Winner 1986), or the social
status of luxury cars. While technology is shaped by the values of society and its
implementation, it is also embodies society’s aspirations for a better future.
Utopian visions of the new technological developments of the 1990s argued
that innovations such as the Internet would be democratizing and lead us into a more
equitable and less discriminatory society. This position contends that the
constraining effects of identity, such as racism, can be overcome by the diffusion and
integration of information and communication technologies (ICTs) into society.
However, this dissertation will argue that, instead of freeing us from discrimination
and social inequality, ICTs may be deepening our relationship to racial identity. ICTs
are not neutral tools in the struggle over racial terrain; their development and
deployment is intimately involved in the shaping the contours of racialization, not
only in local, national contexts, but globally. We are witnessing a “shift or
transformation in the scale of human social organization that links distance
communities and expands the reach of power relations across the world’s major
regions and continents” (Held and McGrew 2000:4). While discussions about

46
globalization have well explored its economic, cultural, and political impacts, the
globalization of scientific research has not received the same amount of attention.
Innovations in communication technologies have provided the material and
informational infrastructure for collaborations across national boundaries. Held and
McGrew describe this aspect of globalization as “a growing magnitude or intensity
of global flows” (2000:3). Cooperation between scientists from different countries is
not a new phenomenon, however, technologies such as the Internet have made the
archiving, transfer of data, interpersonal communication, and collaborating on papers
much more feasible. The key technology that provides the informational
infrastructure for this communication is the database.
Unlike the more visible forms of new media technologies such as the mobile
phone labtop computers, and the iPod, or ‘invisble’ delivery systems such as wi-fi or
the web, the database is a central innovation in the information economy (Elmer
2004:55; See also Cubitt 2000; Loro 1995; Manovich 1999; Poster 1991). Databases
play a pivotal role in the process of sorting and storing data, networking information,
and constructing knowledge. For some, databases enable a "networked "multilogue"
between marketers, consumers, and products” (Loro 1995:55). Consumers can
feedback into the system of product development and sales and become producers.
At the same time, consumer feedback is usually accompanied by giving personal
information as part of filling out a comment form, becoming part of an online
service, or having one’s club cards swiped with every purchase. While companies
are finding out what people like, they are also learning who they are. Knowing your
clientele takes on new meaning with the modern database. Gone are the days of

47
rolodexes. Consumer profiles have become products themselves. Simple query
searches for sorting information have given way to complex techniques of
“knowledge discovery in databases” (KDD) or “data mining” (DM).
Data mining was initially developed by Usama Fayyad as a technique of
searching databases at General Motors to find latent defects company products in the
early 1990s (Zarsky 2003). According to Fayyad, "there were hundreds of millions
of records-no human being could go through it all” (Waldrop 2001:online). The
result of Fayyad’s work at GM and the pattern recognition algorithm he created
became the subject of his doctorial dissertation in 1991. Fayyad’s work with GM is
often cited as the birth of KDD. In the short span of a few years, data mining is now
an integral method used in KDD or Knowledge Discovery in Databases. This
technique has proliferated across disparate domains in society as molecular biology,
the war on terror, and the National Basketball Association. Data mining can be used
to map the human genome, predict terrorist attacks, or assist basketball coaches in
assessing game data. An NBA coach commented that data mining is like having
another coach on the team (Bhandari 1997). Giving data mining human abilities may
not be far off as programs are made up of artificial intelligence, neural networks, and
sophisticated algorithms. Data mining identifies patterns in databases of information.
The technique can search within a given set of parameters and variables given by the
user or it can “discover” hidden patterns and relationships between the data and
make predictions (Zarsky 2003). In short, they can do what they are told by
following a scripted narrative or become storytellers on their own. Internet
companies such as Amazon use databases and KDD along with “recommender

48
programs” to offer customers additional buying options based on their purchases and
other customers who have purchases similar items. This combination of technologies
has also lead to “price customization,” where different buyers are offered different
discounts or coupons based on their profiles and purchasing histories (Turow 2005).
The database as a communicative form breaks down the traditional
transmission model of producer and user by using customer feedback mechanisms.
Manovich (1999) calls the database “a new symbolic form of the computer age”
(81). Like the novel and the cinema that came before, the database is “a new way to
structure our experience of ourselves and the world” (Ibid). One of the main
characteristics of the information age is the seemingly infinite array of texts and
images that litter the sensual landscape in both the material world of our everyday
experiences and the ever-expanding virtual world of the Internet. The Internet is the
ultimate expression of database linking and feedback where consumers are
producers. With its hypertext architecture, there is no linear path through the web.
The linear narratives of old media forms are shattered in the “”anti-narrative logic”
of the web (Manovich 1999:82). The overall narratives are evolving with each user’s
input. With discussion boards, chat rooms, and comment boxes, user feedback
constantly pushes the discursive possibilities and actual boundaries of Internet
websites. Manovich argues that the multiple and potential paths of a database are in
contrast to the linearity of the narrative in old media. Users create “hyper-narratives”
(87). Castells argues that it is not the centrality of knowledge and information that
characterizes the current technological revolution, “but the application of such
knowledge and information to knowledge generation and information

49
processing/communication devices, in a cumulative feedback loop between
innovation and the uses of innovation” (Castells 2000:31). In this system, those who
use technology and those who do things to technology become the same. Put another
way, readers become writers and contribute their own nodes to a network of stories
in an open-ended narrative (Landow 1992; See also Elmer 2004). For example,
Wakeford (2000) suggests that browsing the Internet is also an act of production as
there are multiple pathways within a website and also through the vast digital
network of the web. Some argue, however, that databases are actually passive forms
when compared to surfing the web (Elmer 2001:56). Customers merely input their
data or it is done for them and they are disconnected from the production and
articulation of the information. In terms of marketing databases, this may be true.
The role of consumers is one of strictly inputting one’s details. This perspective
points to the agency of the consumer, and not the nature of the database itself. Data
has to be generated. Again, taking from the Internet, images have to be produced,
audio and video recorded, web pages authored, and algorithms written. Data
indexing has become a new profession. While databases and the Internet have
opened up new possibilities for consumer participation, many have been warning
about the increased surveillance capabilities of new technologies.
Databases have been key technologies in the increasing surveillance of
society. From law enforcement to insurance to marketing, databases are used to sort,
compare, rank, include, and exclude people based on any number of categorical
variables and demographic data. This type of data management has been central to
the modern practice of the management of societies and the process of normalization.

50
A central technique in the concurrent homogenization and individualization of
society is data mining. Oscar Gandy (2002) calls the use of data mining the
“panoptic sort” and suggests that data knowledge discovery in databases is the latest
technology of surveillance. One of the central purposes of information infrastructures
is for surveillance (Lyon 2002, 2003). Surveillance can include government records
of citizens, DNA databanks, customer information collected and shared by
companies, and employers monitoring employees. Different technologies are utilized
to make up the information infrastructures such as CCTV, biometrics, and DNA.
Biometrics is a technology of the present as it archives parts of the body previously
collected from. CCTV is a technology of the present as it watches and records people
and events as they happen. DNA is becoming a technology of the future with the
focus on the predictive possibilities of people’s genetic code. Foucault (1978) has
shown how surveillance was a central technique in the management of populations.
Surveillance has increasingly become dependent on digitized information
infrastructures “which simultaneously made them even less visible and even more
powerful, and also produced some specific kinds of coding” (Lyon 2002:245).
Dataveillance is the use of database technologies for surveillance of a population
(Elmer 2004:75; See also Gandy 1993; Garfinkel 2000; Poster 1990). Again, the
networking of databases strengthens the use of digital technology for knowing a
population for the purposes of risk management and commercial exploitation.
Companies and government have been developing sophisticated data mining
techniques to analyze and create knowledge about consumers and citizens alike.

51
With information becoming central to the operation of businesses and
organizations, data mining is increasingly being implemented in the everyday
operations and cultures of many sectors. One of the primary areas of attention from
scholars, policy makers, and consumers is marketing. Companies employ data
mining to analyze data collected from customers’ profiles and purchasing activities.
Customers’ are ranked and sorted according to a number of variables including the
amount of money they spend at any particular website or their history with a
company. Recently, Joe Turow (2005) recalled a discussion that took place in one of
his classes called “Spam and Society.” A number of the students asserted that they
knew of instances where airline websites were charging new customers lower prices
than returning customers. In 2000, Amazon.com was in the news for consumer
profiling when it offered the same DVD to different customers at different prices.
This sort of “price customization” is made possible through the commercial practice
of tracking customers. Companies collect a number of different types of information
(demographics, geographic location, shopping history, etc.) to try to identify and
target the “best” customers. The others are relegated to lesser services, such when
financial institutions score their clients and use friendlier scripts for the more
desirable ones at their call centers or by excluding them from certain promotions or
coupons (Turow 2005).
The emerging phenomenon of consumer sorting through multiple data points
has also been referred to as weblining (See Chung and Grimes 2005; Gandy 2002;
Stepanek 2000). The term plays on the concept “redlining,” where, historically,
racially segregated communities were cut off from banking and business insurance

52
services. In the late 1990s, as companies began to utilize the web to gather various
sorts of information on customers for marketing and targeting, companies also used
identity data such as race, gender, and class as sorting variables. Companies that sell
marketing data would include such categories amongst others. For example, Business
Week reported that Acxiom, a data brokerage firm, offered a service called Info
Based Ethnicity System that could match someone’s ethnic group against education,
housing, and income (Stepanek 2000). Other, more innocuous forms of data mining
are based on customers’ purchasing history.
Recommender programs utilize data mining to offer products to registered
return customers. The algorithms are based on individual choices and total customer
purchases. For example, if I go to Amazon.com and sign in or simply browse the
website from my home or office computer, which Amazon already recognizes me
through cookies on my hard drive, a number of suggestions will be waiting for me.
For example, after I purchased Database Nation, by Simon Garfinkel, Amazon
‘noticed’ that a number of other customers who purchased the same book also
purchased Oscar Gandy’s The Panopticon Sort. And there are a number of other
books as well for me to peruse. The recommendations are always changing whenever
I re-enter the site. Also, Amazon will send me an email alerting me to new releases
based on my past purchases. For example, just today they ‘noticed’ that Graeme
Meikle purchasers (Future Active: Media Activism and the Internet) are also Charlie
Gere purchasers. Gere’s Art, Time, and Technology comes out in paperback in a
couple weeks and would I like to pre-order a copy?

53
Wal-Mart executives temporarily shut down their recommender program at
the beginning of 2006 when it created a PR nightmare. Shortly after the new year,
customers received emails with links to suggested movies. One of the DVDs on offer
was a Planet of the Apes box set, which was linked to the main website at
www.walmart.com. When customers looked up “Similar Items” at the Planet of the
Apes page, they found all of the films offered were about African Americans,
including civil rights leader Martin Luther King, Jr. Walmart reacted quickly,
shutting down the “item mapping” program, and manually linking the box set to
other DVD sets of Friends, Everybody Loves Raymond, and Star Wars (Kabel 2006).
Part of the damage control strategy of Wal-Mart was to frame this occurrence as a
“malfunction” and list a few other “random” matches that also occurred.
Spokeswoman Mona Williams said that the company was “heartsick,” “horrified,”
and “deeply sorry.” Zarsky (2003) argues that automation of data mining should
mitigate discrimination. When individuals make decisions in situations of
surveillance, such as in the monitoring of a CCTV, they rely on cultural frames to
make their decisions, a case of the “unequal gaze.”
By using KDD, the entire process is carried out via computer algorithms that
present the final result without being manually focused on one group or
another by a human eye or arm and after taking into account all available
information. When applying data mining, the results of database analysis are
balanced, displaying patterns drawn from the population in general that were
chosen according to objective criteria and not subjectively driven. (Zarsky
2003:28)

Through clustering, rather than hypothesis-based classification, decisions are made
by a computer based on several different types of variables. However, the Wal-Mart
example shows how the information that goes into the choices of what clusters to

54
create is derived from socially produced sets of information. Data mining is
dependent on social categories and cultural practices. Any neutrality needs to be
encoded into the program first.

Race and Technology
An increase of interest in the social sciences and humanities into the social impacts
of technology, especially the Internet, and the information society in the past two
decades has accelerated along with innovations and transformations of the
technology. Traditional questions about the nature of society, the relationship of the
individual to social action, and social inequality have been transposed and
reinterpreted for the new social conditions arising from globalization and the
information age (Rasmussen 2000). Barry Wellman outlines three “ages” of Internet
studies (2004). In the early 1990s, a euphoric interest in a new technology that was
just making its way out of the universities turned into the dotcom boom of the mid-
1990s. Wellman situates the beginning of the second age in 1998 when government,
industry, and academia began to undertake systematic studies to move discourse and
knowledge from praise to description. As the Internet grew and the dotcom bubble
burst, the diffusion of the technology moved it from a plaything of computed
scientists to a utility of the masses. Volleys between utopian and dystopian visions of
the computer age abounded. Presently, the third age of the Internet involves much
deeper analysis than standard social scientific data could produce in Age II, “with
more focused, theoretically-driven projects” (Wellman 2004:127).

55
During the latter part of Age II, a small but growing interest in the interaction
between race and technology emerged from parts of new media studies, cultural
studies, and sociology. Evelynn Hammonds (1997) examined new computer
programs that could ‘morph’ people from one race into another in Michael Jackson’s
“Black or White” video and software used to create SimEve, a virtual woman made
up of an amalgam of a number of facial features of different racialized groups.
Rather than deconstructing biological notions of race, she argued that these “new
technologies of race” in fact reinforced centuries old stereotypes of racial difference
and cultural anxieties of miscegenation. Ideologies of bio-race were translated into
the seemingly neutral space of the digital. Kolko, Nakamura, and Rodman (2000)
build on the oppositional stance taken by Hammond that race is neither biological
nor genetic and bring together a number of authors who examine how race is
articulated in cyberspace and new uses of information and communication
technologies in online community formation. In spite of utopian visions of fluid
online identities, the editors argue, “race matters no less in cyberspace than it does
“IRL” (in real life)” (4). Herman Gray (2005) dedicates the last section of his recent
book to the possibilities of cyberspace as a site for black cultural production of a
critical counterknowledge. Gray’s work has long been concerned with the
articulation of cultural representation and social structures in the realm of analogue
(jazz), cable (television), and, now, digital (Internet) space. He argues that popular
cultural forms are now translated, mediated, and transformed into commercial forms
in the hypertext space of the Internet. African American groups such as the
Afrofuturists, a movement made up of a loose collection of artists, musicians,

56
writers, and critics, utilize the global networked architecture of cyberspace to stitch
together and create anew cultural production in the Black Atlantic.
Scattered across the vast geographies of the black diaspora, the only way to
piece together the puzzle of black life and imagination is by way of the (re)
assembly of the cultural equivalent of the Human Genome Project, a sort of
sound archive in which the traces of a black presence can be recut, remixed,
and reassembled not as the original but as something new. This is the work of
the Afrofuturist. The digital information and communication technologies
that make possible the storage, retrieval, production, reproduction, and
manipulation of black soundings are their most important tools. (164)

As mentioned above, scholarship on new media, the Internet, and race is only
beginning to emerge and, surprisingly, quite slowly. Lisa Nakamura’s Cybertypes
(2002) is the first full-length book study of race and technology. She refers to the
transcoding of offline racial ideology and racism online as “cybertyping.” Through
an analysis of MUDs and MOOs, she found that cultural stereotypes of racial
difference were reproduced in the chatrooms. While the Internet allowed for fluid
performances of gender and race, usually in the form of identity tourism where users
could ‘act’ a different gendered or racialized identity, participation often occurred
through stereotypes. White people passing as black, for example, tended to “try on”
and perform what they perceive to be signs of blackness, often relying on caricatures.
Scholarship on race and technology to date has extended issues central to
cultural and media studies. In this regard, the Internet is studied in a manner similar
to old media research. The traditional producer-message-audience model has been
modified to fit new media, primarily the Internet. As discussed above, users are
understood to be producers and audiences at the same time. While old media models
tended to be linear in form, with variations in limited feedback mechanisms, the new

57
media model is an interactive network. Content remains a central object of study as
identity formation shifts from social currents both on and off line. This loose
connection of scholarship between race and technology tends to engage with popular
forms and uses of new media, such as the Internet and mobile phones. However, the
same technologies that appear on the surface of the information age and the ones that
under gird it, such as databases, are ubiquitous across social institutions and private
industries. The social shaping of new media beyond popular culture could provide
crucial insight into the role of technology in identity formation. This type of research
has been the domain of sociology and some aspects of anthropology, especially the
work associated with STS. In turn, cultural and new media approaches could be
utilized in such a study to bring a focus institutional power, and the social
construction of racial identity.

The Information Society, Critical Race, and Science and Technology Studies
Scholarship on the information society examines changes in technology and its
impact on society well, but does not tend to engage questions of race and racism.
Cultural studies and critical race theory has identified many mechanisms in
representation, theorizing the nature of race and the shifting racial order, whether in
the media, popular culture, or the law, that make up racialized identities. However,
this body of work has not paid particular attention to emerging mechanisms shaped
by new technologies and the information society. As Leger (2005) has pointed out,
there is a disjuncture between the cultural studies of science and medicine and
science and technology studies (STS) in regards to the object of investigation. The

58
former is interested in the representation and production of medical and scientific
knowledge (Treichler 1999; Treichler, Cartwright, & Penley 1998; Haraway 1997)
while the latter places priorities on the production of scientific knowledge in labs,
clinics, or the field (Rapp 1999; Rabinow, 1996; Latour 1999; Pickering 1995;
Latour & Woolgar 1990; Fujimura 1996). This dissertation is concerned with both
foci: How scientific knowledge produces and represents race through the shaping of
new communication technologies.
In studying how science and scientists construct racial knowledge one may
see an alternative in STS to a cultural studies approach that largely focuses on
representation in media and popular culture. However, while STS has developed a
critique since the 1970s of the construction of knowledge in scientific settings,
essentially unveiling the black box of science, and the social construction of
technology, race has largely been absent. M’Charek has noted that while studies of
gender and technology have become well established in STS, there is a virtual
absence of studies about race and technology (2005:165). A survey of a leading
journal in STS, Science, Technology and Human Values, shows only a handful of
articles on race since 1999.
3
The last meeting of the largest STS association, the
Society for the Social Study of Science, in 2005 still designates a “race” panel, a
special session rather than an object of study in the fabric of STS.
4
A recent survey

3
STHV articles and abstracts are only available online from 1999 onwards.
4
The 2005 program for the SSSS conference held in Pasadena, California, shows
only two panels that deal specifically with race or racialization. One panel is simply
titled “Race” (the race panel) and the other, “Race, Genetics and Diseases: Questions
of Evidence, Questions of Consequence”. The 2006 program to be held in
Vancouver, Canada, was not available at the time of writing this dissertation.

59
text on STS by Bauchpies et al (2006) only mentions race a few times in their
discussion of the field. Race is treated in an “add-and-mix” fashion and is usually
sandwiched in a manner typical to STS, “gender, race, and class.” Apparently, in the
decades of STS research, there is not enough material on race, science, and
technology to warrant a full chapter discussion. This seems odd considering the work
of Haraway (1989, 1997), Harding (1998), and Hammonds (1997), for example. One
of the defining characteristics of cultural studies since the late 1970s has been its
focus on race along with other identities of difference, class, gender, and sexuality.
What critical race theory has failed to address is the staying power of race as an
organizational concept in society, the lived experience of everyday people and a
fundamental classification of knowledge in biomedical science. As Fausto-Sterling
emphasizes, race is a “concept that refuses to die” (2003:2). The trouble quotes have
gone up and come down and race is still with us, no matter how much social
construction, deconstruction, historical materialism, and postmodernism has been
thrown at it.
Scholarship on science, race, and society has investigated the relationship
between racial ideology and scientific knowledge in terms of eugenics and race
(Duster 2003a; Gilman 1985; Gilman 1988; Haraway 1997; Holtzman 1999; Kevles
1985; McLaren 1990; Paul 1998; Van Dijck 1998), intelligence (Gould 1996;
Lewontin, Rose, and Kamin 1984), and the ownership of human DNA (Nelkin and
Andrews 2003; Nelkin and Tancredi 1989; Poudrier 2003). Recent scholarship
focusing on forensic science and DNA racial profiling (Cho and Sankar 2004; Duster

60
2003b; Ossorio and Duster 2005; Sachs 2003), pharmacogenomics and BiDil (Duster
2003a; Kahn 2003, 2004; Lee 2003; Lee, Mountain, & Koenig 2001), and genome
projects (Duster 2005; Reardon 2005) has examined the reemergence of race as a
biological category in biomedical research and the implications of this process for
health, identity, and society. However, crucial emerging questions need to be
answered about how current trends in DNA research are utilizing communication
technologies and old conceptions of race to produce new types of racial knowledge
in the socio-political contexts of the information society and the dominant racial
ideology of color-blindness. Paul Gilroy (2000) suggests that new scientific
technologies are “prosthetically extending sight into nano-scales and can be linked to
the impact of digital processing and other allied approaches to the body that allow it
to be seen and understood in new ways, principally as code and information… skin,
bone, and blood are no longer the primary referents of racial discourse” (44, 48; See
also Nelkin and Tancredi 1989:15). Race is being constructed in terms of genetic
information, rather than the traditional markers of skin, culture, or nation. The new
genetic research is bypassing the skin in its search for the truth of who we are at the
molecular level. Digital imaging is re-imagining the body as code and information,
rather than flesh and blood.
The boundaries of “race” have now moved across the threshold of the skin.
They are cellular and molecular, not dermal. If “race” is to endure, it will be
in a new form, estranged from the scales respectively associated with
political anatomy and epidermalization. (Gilroy 2000:47)

Gilroy refers to the process of constructing race at the genetic level as the
molecularization of race (See also Fullwiley forthcoming). While discoveries are

61
being made to cure before untreatable diseases, the interaction between culture,
technology, and science in genomic research has opened up old questions about the
biological validity of race, the role of race in science, and science’s role in the
construction of race. Instead of finding evidence that we are all indeed the same
beneath the skin, genomic research is attempting to discover new (and not so new)
differences based on old assumptions (Duster 2003a:146). Both Gilroy (2000) and
Duster (2003a) point to the relationship between the knowledge of genetic research
and the technologies that make the body, below the skin, able to be seen.
Genomic research is only in the beginning stages and the social uses and
consequences of this new ‘truth’ of the human body are in their infancies. Recent
developments in DNA research concerning the biological basis of human
differentiation shows that new scientific technologies are envisioning the body as
genetic code, rather than skin, hair, and facial features. Race is being constructed in
terms of information, rather than the traditional markers of skin, culture, or nation.
Genomic research of identified population groups is producing scientific, medical,
and forensic knowledge about variation between groups and how genetic variation
may contribute to differential behavior. Each of the above research traditions has
their own approach to the study of race, technology, science, and society. Separately,
however, they miss a crucial new trend that is emerging with the impact of new
communication technologies on the social construction of racial identity.

62
The informationalization of Race
A new emerging process of racial signification is characterized by the creation and
deployment of informational codes that do not make reference to phenotypic notions
of race (color of skin, shape of eyes, texture of skin), culture, or national identity, the
traditional markers of racial difference. Instead, the social construction of race is
becoming a process where information is the material by which social, economic,
and political meaning is worked on. I call this phenomenon the informationalization
of race. Race as culture emerged from race as biology and race as information comes
from both. Social organization, symbolic flows, and human action are produced
racially without reference to biology or culture. The informationalization of race is
not replacing its reference to ethnicity or skin colour. It is moving alongside them
while continuing to use them as points of signification. Meaning and action in social,
political, juridical, scientific, and other institutional contexts is still overly dependent
on skin colour and cultural and national markers. While race is by no means finished
with phenotypes or culture, it is not always reliant on them either.
What distinguishes race as information from other modes of racialization is
the transformation of society due to globalization, the new economy, and ICTs.
Racial identity, meanings, and structures are being created in terms of information
collected, stored, and analyzed through the use and shaping of communication
technologies.
5
Of particular interest are innovations in computing, databases, data

5
Information and communication technologies is an umbrella term that refers to
recently developing forms of electronic communication, including computers,
microprocessors, databases, digital imaging systems, the Internet, wireless telephony,
satellites, fax machines, and fiber optics. They are intentionally separated from

63
mining, and the Internet. I take the position that new communication technologies
increasingly make up central systems in which racial meanings are created,
transformed, and destroyed, to borrow from Omi and Winant, and social action is
enabled and constrained. This networked system is not simply a delivery tool for
ideas and meanings, its very structure and scope, both hypertext and globally linked,
is productive of new mechanisms of racialization. Whereas traditional conceptions of
race have been formed around points of culture or biology, modern procedures of
collection, classification, storage, and processing of information are having racial
outcomes. Instead of examining representation in television, film, and news, the
concept of informationalization of race focuses on the discourses produced in the
hypertext communication environments of computers, databases, knowledge
discovery in databases (KDD) or data mining, and the Internet. Before we can look
at the technology, however, the term “informationalization” requires some
elaboration.
The term “informationalization” builds on the insights of the literature on the
information society that examines the shift in societies from industrial economies to
services economies due to the restructuring of capitalism, the new technological
paradigm, and globalization (Webster 2002). Informationalism is a “specific form of
social organization in which information generation, processing, and transmission
become the fundamental resources of productivity and power” (Castells 2000:21).
Communications and technological systems are increasingly the sites where power is

traditional communication technologies, such as the telegraph, analog telephones,
and television, as they emerged during globalization.

64
enacted, circulated, and contested. Whether power is nowhere and everywhere in a
web of discourses (Foucault 1978) or held and exerted within the structure of the
network (Rentenan 2005), different forms of media, old and new, makeup the
technological, economic, political, and cultural spheres of influence. In the changing
communication systems, the media plays a central role in representation and
consumption (Collins 2004; Gray 2005). For example, Mackenzie (2003) questions
the usefulness and point of tracking computational processing of sequence data:
“Does not bioinformatics merely support the more decisive intellectual, social,
political, cultural and economics events associated with contemporary biology and
genetics” (316)? Mackenzie’s question points at key issues to STS and new media
studies. Understanding the relationship between technology and society or science
and society does not only mean looking to the practices within scientific and
technological development. A sociologically informed analysis looks beyond these
internal questions to the social context in which institutions operate. The turn to
information in science in other domains in society expresses particular
transformations in the location and production of power. The research in this
dissertation examines the nature of information and its relationship to power and the
‘reality’ of the social world.
The concept of information denotes a neutral set of facts, data, or
observations. When information is networked and takes on the form of an
information infrastructure, such as when databases are compiled and linked or the
seemingly endless pathways of the World Wide Web, it should not be treated as
having a simply reflective role in the social world. Like other media forms (news,

65
television, film) information infrastructures do not just support cultural, political,
social, and scientific processes. They play a constitutive role.
It is politically and ethically crucial to recognize the vital role of
infrastructures in the ‘built moral environment.’ Seemingly purely technical
issues like how to name things and how to store data in fact constitute much
of what we have come to know as natural. (Bowker and Star 1999:326)

Technologies and the classification systems that utilize them tend to make invisible
the myriad of decisions that create them.
That is, the arguments, decisions, uncertainties and processual nature of
decision-making are hidden away inside a piece of technology or in a
complex representation. Thus, values, opinions, and rhetoric are frozen into
codes, electronic thresholds and computer applications. (Bowker and Star
1999:187; See also Garfinkel 2000)

Put another way, the seemingly descriptive representations derived from information
infrastructures in fact naturalize a whole set of practices, procedures, and ideological
premises.
Looked at historically, information seems basic to social life. In oral cultures,
stories and ancestral anecdotes ensure that people know about reality, and
some of this involves what might be called ‘natural’ signs to do with, say,
weather or hunting. In modern, literate cultures, artificial signs proliferate,
and are frequently associated with social order itself. Signs tell us of distant
events, places, persons and processes. Information is relational, connecting
by reference persons and things. Intelligence is assumed, as are the reality of
things and contexts. But whereas information might once have thrown light
on reality, or even, through instructions or recipes, contributed to the
transformation of reality, once technological devices become the predominant
carriers of information, the distinctions blur. (Lyon 2005:225)

The oral stories and cultural signs that have told us and continue to tell us about the
social order, including the racial order, are now not only mediated but created in
communication technologies but through informational codes. The
informationalization of race acknowledges that race as a structuring device in society

66
has not diminished in importance with the information age and has continuity with
modernity. While at the same time, this research seeks to examine how innovations
and applications of communication technologies and the rise of information have
produced new mechanisms of racialization in a post-civil rights context. There are a
number of institutions where we can see the informationalization of race at work,
such as law enforcement, biomedical research, insurance, and marketing. While each
would have their own set of technologies for information storage, classification, and
surveillance, they have increasingly employed a similar array of technologies to their
own institutional needs. For example, where the microscope has been a central
observational tool in the biomedical sciences, actuarial tables in insurance, and
fingerprinting in law enforcement, data mining techniques and the technological
infrastructure that it requires is commonly used across these different institutions.
Communication technologies play a leading role in the everyday practices and
organization of a range of social institutions and industries. This dissertation will
develop a case study of human genomics to understand the specificities of the
informationalization of race in one institutional setting. While scientists debate the
accuracy or inaccuracy of scientific data, which is the outcome of computational and
statistical routines, we might back up and examine how those outcomes stitch
together cultural assumptions, molecular particles, microprocesssed bits and bytes,
and historical context.

67
Conclusion
This chapter has explored the theoretical terrain of the areas of race and technology
and suggested how social and technological transformations have produced a new
form of racial difference, the informationalization of race. The informationalization
of race articulates how race is being constructed out of information and
informational processes that are the product of technological developments and
innovations, globalization, and colorblindness. Investigating strategic sites is central
to understanding what the key mechanisms are in this process and how and where
they are taking place. As I outlined above, genomics is a critical site of knowledge
production of the informationalization of race. Genomics and the HapMap Project
are products of the information age. This dissertation’s examination of the
informationalization of race in genomics is three-fold. The following chapters will
examine the technological, legal and institutional, and cultural dimensions of
genomics using the HapMap Project as a cases study. Technological innovations are
increasingly becoming ubiquitous in the development and management of most
domains of society. It is becoming difficult to discuss social organization without
including the role of technology. However, technological innovation does not take
place in a vacuum and needs to be understood in terms of the changing historical
frameworks, especially in terms of regulatory changes in governance and the law.
Equally important in this contest over the structure and meaning of genomics is
culture. Discursive frameworks about race shape not only how the data is reported
and knowledge is produced, but influence the questions being asked, the designs of
studies, especially big science genome projects such as HapMap, and the meaning of

68
the human body at the molecular level. A first level of research design may pose
culture as the dependent variable, viewing racial categories as being acted upon by
scientific processes. However, cultural understandings of race deeply influence how
the scientific process is carried out.

69
Chapter 3
Techno Genomics:
The Digital Shaping of Biology and the Rise of the Database

Through the 1970s, a small group of individuals began to realize that
computers and sequence information were a natural marriage. Bride and
groom struggled to overcome vast cultural differences. Computer scientists
and molecular biologists traced their lineage through difference tribes, with
vastly different norms, and only a few hardy souls could converse in both
languages and command respect in both communities. The database that
stored sequence data became their meeting ground.
(Cook-Deegan 1994:285)

The fact that the development of computer technology, with its demands on
information theory, has occurred contemporaneously with the growth of
molecular biology has not merely provide the physical technology, in
instrumentation and computing power, without which the dramatic advances
of the decades since the 1960s would not have been possible. It has also
given the organising metaphors within which the data was analysed and
theories created.
(Rose 1997:120)

The discovery of rDNA in 1973 provoked a paradigm shift in the institutional goals
of biology. Instead of studying how things worked, scientists could begin to
manipulate the molecular structure of organic life through the transplanting of genes.
This watershed moment in the history of biology and genetics marked a change in
biology from an analytical science to a synthetic one and the beginning of genetic
engineering (Krimsky 1999). New organisms could be manufactured and biological
products could be mass-produced. The cutting and splicing of different strands of
recombinant DNA opened up infinite possibilities and the imaginations of scientists.
The implications for science, society, and governance were not lost on scientists in
the biomedical sciences. Foreseeing the gravity of re-producing and altering nature, a

70
number of scientists met at the Asilomar Center, nestled in the evergreen trees and
the sea mist of the west coast of California in 1975. “Asilomar,” as it has come to be
known (which means ‘refuge by the sea’), was organized by a group of scientists
lead by Paul Berg, a Stanford biologist and an early leader on potential risks of
rDNA, and attended by over 140 participants, mostly made up of biologists, but also
some doctors and lawyers and more than a dozen journalists (Barinaga 2000). This
was a particular moment in the history of biology where scientists engaged in self-
governance and reflected on their social responsibilities.
The main discussions revolved around the creation of guidelines and
principles for the safe handling of recombinant DNA molecules and how to contend
with possible government regulation. On the one hand, scientists were worried that a
dangerous bacterium could walk out of a lab on the bottom of someone’s foot. On
the other, the institutional fears of the scientists were that Congress would step in to
allay public fears about the unknown consequences of genetic engineering. They
were concerned about how the type of social panics, as seen in response to chemicals
such as lead or asbestos, would provoke the government to institute strong and
inflexible regulations from without rather than from within the scientific community.
Government directives would weaken the legitimacy of molecular genetics and
hamper its progress, thus reducing autonomy in assessing and managing risk and a
principle research method (Krimsky 1991). The key body to manage the guidelines
that came out of the Asilomar conference and alleviate the concerns of government
regulators and the public was the National Institutes of Health. According to one of

71
the co-organizers, Robert Sinsheimer, Asilomar “helped in many ways to launch the
complex world of biotechnology we know today” (Quoted in Petsko 2002:1).
The world of biotechnology has indeed become a complex one. The issues
that scientists discussed at Asilomar, while progressive in terms of the ethics of
scientific research and self-governance, were much simpler in 1975. The academic
oriented, inward thinking concerns of scientists studying and creating a technology
that was just beginning to be imagined have moved outside the academy into
biotechnology companies. While a number of the academics have joined the private
sector, the entire discipline imagines itself beyond the university lab as the primary
location of the enterprise. Genetic technologies have developed alongside and with
innovations in information and communication technologies. Government bodies,
activists, and corporations have dismantled the type of self-regulation that scientists
were used to operating in. Issues of ethics, law, and society are no longer the sole
domains of the scientific community. A few of the original attendees returned to the
seaside center in 2000 to discuss the current state of genetic engineering. Only this
time, the meeting was organized by internationally recognized legal scholar, Alex
Capron, and included panels featuring sociologists, journalists, and activists from
NGOs (Barinaga 2000). Finally, the scientific community has also been forced to
incorporate cultural concerns into the core of their discussions. Previously
marginalized groups such as women, gays and lesbians, and racial minorities have
insisted that the scientific process become more attentive to social concerns and
more inclusive of diversity both in the minting of new scientists and expanding the
core of research subjects and issues outside the white male norm. Bioethicists,

72
advocacy groups, and scholars across the humanities and social sciences are active
members of large scientific projects, especially ones that pertain to human genome
research such as the HapMap Project. Biologists can no longer seek a ‘refuge by the
sea.’
Genomics is an important domain in which to study the informationalization
of race as it has come to embody these technological, institutional, and cultural
changes. Building on the history of population genetics, physical anthropology, and
molecular biology and aided by developments in communication technologies and
computer science, genomics is the “comprehensive study of all genes” (Interview
1001; Interview 1022). In contrast, the traditional domain of genetics is on inherited
differences in specific genes, such as from parent to child. The “omics” in genomics
is to be comprehensive, while genetics focuses on individual genes. A genetic
approach to studying the long strands of DNA that are the building blocks of life
differs from genomics in that the objects of study are individual genes. The HapMap
project will allow for an in-depth analysis of the building blocks of this dissertation
and the concept of the informationalization of race, the three broad areas of change
(technological, legal/institutional/cultural) that make up the substance of the next
three chapters. Chapter four will map out the changing landscape of regulations that
govern genome research in particular and biotechnology more generally. Chapter
five will explore the culture frameworks and discourses of race that are being
negotiated around genomics.
The present chapter will examine the technological changes in genome
research due to the convergence of the biological revolution and the new

73
technological paradigm. The outline of this chapter is three-fold. First, I explore how
developments in biology have interacted with innovations in computing to become
an informational science. Second, I show how databases and data mining techniques
have become central tools to store, search, and analyze digital DNA data in the
convergence of biology and information technology. There has also been a
proliferation of DNA databanks at the national and global levels. Finally, I will
discuss how the Internet has changed the practice and culture of genomics through
the networking of labs and scientists, the form and function of genetic data, and
issues of intellectual property.

From Analytics to Synthetics, From Wet to Dry labs: Biology Becomes an
Information Science

Many molecular biologists who welcomed the Human Genome Initiative
undoubtedly believed that when the genome was sequenced, everyone would
return to the lab to conduct their experiments in a business-as usual fashion,
empowered with a richer set of fundamental data. The developments in
automation, the resulting explosion of data, and the introduction of tools of
information science to master this data have changed the playing field
forever. There may be no ‘lab’ to return to. In its place is a workstation
hooked to a massively parallel computer producing simulations by drawing
on the data streams of the major databanks and carrying out ‘experiments’ in
silico rather than in vitro. The results of biology’s metamorphosis into an
information science just may be the relocation of the lab to the industrial park
and the dustbin of history.
(Lenoir 1998:41)

The transformation of biology was clearly on the minds of the scientists who met at
Asilomar to discuss how to protect their discipline from the government as well as
society from new and possibly dangerous bacterium. Like the damp surroundings of
the conference though, their visions were probably those of the wet lab. Not far from

74
Pacific Grove another revolution was underway in the Silicon Valley that would
make possible the dramatic developments in molecular biology that we are beginning
to take for granted today. An electronic revolution that clustered around a number of
different technologies, such as the microprocessor, microcomputer, resulted in a new
technological paradigm that has become an inextricable aspect of the information
age. They have become “two of the most important modern technologies, gene
technology and information technology” (Burnett and Marshall 2003:57). Biology
has not only developed alongside the electronic revolution, but it has fused with
innovations in computing, software, and data management, becoming as much an
information science as a biological one (Capra 2002; Marturano 2003, 2004;
Interview 1011). In short, the wet labs have increasingly shared (and given up) space
with the dry labs of bioinformatics, computational biology, and genomics. An
example of this new configuration is the newly built Molecular and Computational
Biology Building at the University of Southern California. The focus of the building
is genomics and brings together molecular and computational biology research,
which had already merged formally. The first two floors house the wet labs of
molecular biology. On the third floor are dry labs designed for an emerging type of
biologist schooled in both experimental biology and computational biology. Finally,
the fourth floor comprises of a number of computational suites for scientists to mine
the loads of data produced by the first three floors. When it opened, USC university
President Sample commented that the new building is not simply about new office
and lab space, it “will be a complex and interdependent ecosystem of scientific
creativity and invention” (Emerson 2005:Online). In these new spaces of scientific

75
innovation, biologists, computer scientists, and engineers work side by side,
borrowing methodologically, theoretically, and culturally. In the process, biology
becomes bound up in data.
As modern science has increasingly relied on computer simulations,
computational models and computational analyses of large data sets, scholars argue
that this process has led to a theoretical convergence between information and
genetics technological fields (Gezelter 1999; Haraway 1997). Since the 1970s, there
have been massive transformations in the fields of biology and micro-electronics that
have grown from convergence to interdependence. Key innovations in biological
research into genetics and the burgeoning field of genomics have only proceeded
because of innovations in supercomputing. Interviewees from the HapMap project
unanimously agree that there could be no new genetics without computing science.
Marturano (2003) suggests that human genome projects are not only biomedical
projects, but bio-informatic ones as well. Genetics, considered to be the realm of
human blood and protein function, is not entirely independent from microelectronics,
the world of processors and bytes. Genetics technologies are obviously information
technologies, since they are focused on the decoding and eventually reprogramming
of DNA, the information code of living matter (Burk 2002). Genetics would not have
advanced at the pace or with the intensity it has in the past twenty years without
advances in computing. Further, specific projects, such as the HGP would simply not
have been possible without the aid of computers, especially database technologies
and data mining techniques that were not even in existence in the early conceptual
stages in the 1980s.

76
A philosophical problem arises from the view that DNA and the Human
Genome are pure informational concepts. The convergence between the biological
and computing might be argued as an instrumental association with the massive use
of computer technologies in biology. That is, computer technologies are simply used
as tools and are purely external entities to biology. However, Holdsworth (1999)
suggests, ‘‘It is not just that computer tools are rather convenient for doing genomics
and protein sequencing. Rather these… disciplines have re-organised themselves
around the bioinformatics paradigm’’ (85). This convergence has incorporated
Shannon and Weaver’s (1949) notion of information that views all information as
capable of being broken down into discrete quantifiable bits that can be measured
and analyzed (Lyon 2005). Genome science begins from the position that the human
body and, specifically, the human genome, can be perceived as information. The
genome is a long string of information that can be managed and understood through
identification and analysis of the different parts of the genome, whether those parts
are whole genes, haplotype blocks, single nucleotide proteins, or base pairs.
Genome science has emerged as the next wave of human scientific research.
Derived from molecular biology, genomics became widely known for the Human
Genome Project. As Francis Collins, the Director of the National Human Genome
Research Institute (NHGRI; one of the National Institutes of Health) states about the
future of the genome,
Genomics has been at the forefront of giving serious attention…to the impact
of science and technology on society. Although the major benefits to be
realized from genomics are in the area of health…genomics can also
contribute to other aspects of society. Just as the [Human Genome Project]
and related developments have spawned new areas of research in basic

77
biology and in health, they have also created opportunities for research on
social issues, even to the extent of understanding more fully how we define
ourselves and each other. (2003:483)

Evelyn Fox Keller (2000) has illustrated how gene talk became firmly embedded in
scientific as well as popular discourse over the last century; the twentieth century
was the century of the gene (See also Gelbart 1998). As genomics moves from
mapping to function, from being just a tool to a working process and as scientific
research and discourse turn to the genome to find solutions to widespread health
problems and the code the human network, then the twenty-first century could quite
possibly become the century of the genome.
One of the world’s foremost geneticists stressed to me the under appreciated
role that communication technologies play in the development of genomic research
(Interview 1001). He often points this out in his lectures to students. The amounts of
data that are required to map the human genome and haplotype blocks have
previously made research of this scope and magnitude prohibitive (Interview 1005).
Well I don’t think we would be able to do it if it wasn’t for technology.
Because the genotyping technology is so advanced and so high through-put
and there is such a large amount of data that if we didn’t have the computers
and bio-informaticians and databases to process all that data, it wouldn’t be
feasible by hand. (Interview 1003)

This opinion was widely shared, if not unanimous, among HapMap participants.
When asked about the impact of communication technologies on genome research
the first words used to describe them were “central” (Interviews 1001, 1002),
“essential” (1005, 1009, 1013, 1014, 1017), “paramount” (1010), “fundamental”
(1016), and “foundational” (1005).

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Oh they’re absolutely essential. I mean we couldn’t be doing nearly what
we’re doing in genomics without computers. In fact, I think they’re
foundational. (Interview 1005)

You just cannot do genome science without the use of the technologies that
have been developed to handle the very workload. (Interview 1006)

[T]he Human Genome Project would essentially be inconceivable and not-
doable without…computer technology. (Interview 1014)

A vast network of scientific centers are scattered across the globe. Genome scientists
piece together the “blueprint of human life” by way of the assembly of the haplotype
blocks of “major geographical groups,” as they are named in the project, a database
of the evolutionary traces of DNA histories. Each team of the HapMap project
sequences a portion of the genome, such as Chromosome 2 and Chromosome 4p by
the McGill University/Genome Quebec group in Canada, Chromosome 7p by the
group at Washington University in St. Louis and the University of California at San
Francisco, or others, both academic labs and biotechnology companies, in China,
Japan, and the United Kingdom. The data from each group is collected, curated, and
stored for distribution in a database in Bethesda, Maryland, at the National Institutes
of Health and released online over the Internet for public access within twenty-four
hours. The most important tools for these scientists are the digital technologies that
enable them to produce and reproduce, store, retrieve, and manipulate genome
sequence.
Developments in computer power, databases, and the Internet have made the
archiving, management, and distribution of the vast amount of data possible not only
in local labs and internal networks, but on a global scale. Genome research produces
enormous amounts of data that bench scientists could never analyze by hand and

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“would be simply impossible to process without computers” (Interview 1009). This
includes statistical calculations, which have required the development of new
algorithms and software. Biologists are not traditionally quantitative and are
unaccustomed to working with such large data sets.
And there has been over the last decade increasing, a trickle first and then
increasing in flux of computationally sophisticated people into the field who
have brought with them sophisticated computational methods for data mining
and data analysis. But there’s still a big divide that exists between most
working biologists and any of those methods. And so it requires not just an
intellectual shift, but also a real cultural shift because biologists are used to…
the limiting step being their ability to collect data with their hands. (Interview
1001)

This shift from observation to a “data-bound science” (Lenoir 1999:35) is at the core
of the transformation of biology.
The cultural shift in biology originates in the team-based approach to
genomic research. Genomics generates enormous amounts of raw data that a single
person cannot make sense of on her own. A scientist at the NIH working on HapMap
recalled how painfully slow the labor of aligning sequences was when he first began
genetic research (Interview 1016). Computational techniques such as computer
algorithms and data mining have made significant impacts on this process.
Sequence reads tend to be fairly short stretches of 500 to 1000 base pairs of
DNA, and yet you want to make a single, continuous sequence read for an
entire chromosome, consisting of hundreds of millions of base pairs of DNA.
And so that software was, without doubt, absolutely essential. (Interview
1014)

The days of the individual scientists working in isolation in her lab, scribing
notes and models in a notebook, have given way to multi-disciplinary teams of
researchers. For example, the HapMap Project is made up of biologists, geneticists,

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statistical geneticists, doctors, legal scholars, bioethicists, bioinformaticians,
anthropologists, and sociologists. As this consortium of experts shows, a broad range
of knowledge is needed to conduct large-scale genome research. As the inclusion of
social scientists in the list of personnel shows, a significant part of the research team
belongs to the Ethical, Legal, and Social Implications (ELSI) committees. Team-
based projects are often focused on a particular problem and located in a particular
lab or located across a number of different centers, often in different countries,
sharing information in a common, digital database. The database itself may be
curated in a university lab, accessible only to the project group, or in a centralized
location, such as the National Institutes of Health, open to the public, and accessible
to various research teams around the world. As the interviewee commented above,
genomics requires computers and programs that biologists are not familiar with.
Biologists and computationalists, or bioinformaticians, work together, but with skill
sets that, usually, do not overlap.
…the computationalists, who often have techniques, don’t have any idea
what the interesting questions are because they’re not biologists. So it
inevitably leads to maybe some day, all this will become codified enough that
single people, single investigators will have all the capabilities they need to
do all the genetics and no biology and no computation. Probably there will be
a new generation trained that can go back to being more individualistic. But
for now there’s no one out there who’s really good, who knows all these
things. And it may be that no one ever does know all these things, because
there’s too many things to know. (Interview 1001)

Teams can be as small as three or in the hundreds as in the case of big science
projects such as the Human Genome Project. While the cultural shift in biology is
embodied in the team-based approach, institutionally the domain has become ‘big
science.’ Big science projects differ from team-based projects simply in the sheer

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numbers of researchers needed to tackle a project and the cultural shifts in the latter
described above. Big science can be team-based, such as the HapMap Project, or it
can be mono disciplinary (Interview 1001). Evidence of biology becoming big
science includes the standardization and routinization of particular practices (Jordan
1998). The farming out of “cookbook techniques,” that is, techniques that have
become routine practices and procedures, is viewed as a sign of a successful science
(Gilbert 1992:93). Biotechnology companies now specialize in practices such as
sequencing and provide outsourcing for techniques that used to be performed by
skilled researchers. There are a number of biotech companies, such as Illumina,
Sequenom, and ParAllele, which were genotyping centers in the HapMap Project.
The industrialization of biology has not completely left academic labs. A senior
scientist at one of the world’s largest biotech companies expressed concern that
graduate students were not only in the lab to learn the craft of science but as a “pair
of hands” (Interview 2001). They are being used “like cheap labor” to further some
professors’ own, private sector oriented goals.
The interaction of computer science and molecular biology that characterizes
genomics has moved biomedical research to computational biology (Interview
1006). While scientists’ definitions of genomics focus on the way that they approach
studying the genome, they seldom include a nod to technology even though genome
study has moved from theory to application only through digital technology. Leroy
Hood, one of the scientists who attended the early Human Genome Project meetings
in the 1980s and the leader of the CalTech team who invented one of the most
important technological developments in genomics, the protein sequencer, noted that

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many biologists are “indifferent to technology - they use it, but they don't really see
it as a fundamental part of biology. Indeed, it is new or more sensitive technology
that can open up new horizons in biology” (Hood 2001:Online). While an argument
for technological determinism would be misguided as the science and motivations
derive from human actions, it is clear that genome research in particular and
molecular biology more generally are not only expressions of advancements in
scientific discovery, but of computing technology.
I think that in fact in many circles genomic research has been discussed as
developing out of DNA technology. I think it is fair to say that the entire
concept of genomics, which is really one of data rich studies in biology
where you have archival quality data that is comprehensive and is shared
freely, is as much about, if not more about, computers and the Internet as it is
about DNA technology. (Interview 1001)

Accordingly, a number of respondents agreed that the impact of genomics would be
trivial compared to what it is. Two technological developments in particular were
commonly discussed among HapMap participants, databases/data mining and the
Internet. New databases have been designed to store, analyze, and distribute the data
and findings. Data mining techniques and “large, easily-accessible databases that
would allow the extraction and comparison of data was absolutely essential for being
able to put together any kind of sequence database” (Interview 1014). The Internet
enables genome projects to move data between global locations and labs in the same
building as well as provide open access from anyone interested in the information.
The HapMap Project follows an open access approach to data by making the findings
available publicly on the Internet within twenty-four hours of the data being
collected from the multiple sites. The Internet has increased the ability of scientists

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to undertake international collaborations. Also, new technologies have played an
integral part in restructuring paternal models of consent.

Data Mining the Code of Human Life

One consequence of the human genome project is that we will see more and
more clearly how connected all life really is… The data base of the human
genome… promises to reveal patterns of genes and to show us how we
ourselves are embedded in the sweep of evolution that created our world.
(Gilbert 1992:97)

Through the 1970s, a small group of individuals began to realize that
computers and sequence information were a natural marriage. Bride and
groom struggled to overcome vast cultural differences. Computer scientists
and molecular biologists traced their lineage through difference tribes, with
vastly different norms, and only a few hardy souls could converse in both
languages and command respect in both communities. The database that
stored sequence data became their meeting ground.
(Cook-Deegan 1994:285)

The information processing techniques of data mining and knowledge discovery in
databases have become widely used in biology, and especially in human genetics and
the emerging field of genomics. Data mining is the digital technique of reading
databases, creating stories, and drawing pictures from their content. Hine refers to
them as “new communication regimes, new forms of collaboration and new spatial
organizations for science” (2006:270). Biologists began using computers to study
DNA in the 1970s. With the development of computer processors, software, and
complex algorithms, scientists have been employing the same techniques for
analyzing data in databases as commercial companies and governments. This new
scientific process is called “discovery science” and is largely credited with the
success of the Human Genome Project and no the HapMap Project. Much like the
basic assumptions of data mining described above, discovery science is “the idea that

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you take an object and you define all its elements and you create a database of
information quite independent of the more conventional hypothesis-driven view”
(Hood 2001:Online). In contrast to the meticulous method of making theoretically
sound hypothesis before collecting and analyzing data, discovery science is more of
a collect first and asks questions later approach. According to Leroy Hood, the
genome posed such technological problems not only with sequencing and mapping,
but also in computation and analysis that a new paradigm, discovery science, was
needed to tackle the enormous obstacles posed by creating and analyzing a
comprehensive database. The process of discovery science looks quite similar to
“'enterprise' systems in other domains such as eBusiness and eCommerce where
linking diverse databases and processes (customer data warehouses, billing and
auditing software, just-in-time scheduling and inventory systems, reporting systems,
delivery tracking processes, online web sales, etc.) has been the object of intense
activity over the last decade” (MacKenzie 2003:328). Innovations in database and
computer technology utilize shared application across disparate sectors such as
insurance, book sales, and genomics.
KDD is a central technology in genomics where theory meets practice.
Lenoir (1999) describes computational biology and bioinformatics as the theoretical
and instrumental/experimental components of genomics. The database is where they
come together to construct scientific knowledge through the core technique of data
mining. The development of this relationship was key to the success of the Human
Genome Project. The data mining technique developed by Craig Venter’s team at
Celera, the private firm that co-sequenced the human genome, is the Shotgun

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method. The Shotgun method takes random cloned sections of a genome and
sequences them. A computer takes a number of these partial sections, rearranges and
stitches them together to form a complete sequence. Since the geneticist works at an
abstract level, much like software developers, the time-consuming work of mapping
is eliminated. Venter’s contribution to the mapping of the human genome sped up
the actual process of completing a working map of the human genome, and
compelled Celera’s public counterpart the HGP, led by Francis Collins, to accelerate
their approach. The result was an announcement by President Bill Clinton of the
completed draft of the human genome in 2001, four years ahead of schedule. Despite
the joint announcement that was largely a media event featuring Collins, Venter,
President Clinton, and Prime Minister Tony Blair beamed in via satellite, the Human
Genome Project was the most spectacular manifestation of the methodological,
theoretical, and technical convergence between the fields of genetics and
information.

Databases, dbSNP, and the DNA banking system
The databases built from the Human Genome Projects are only two among a boom
of DNA databases, databanks, and population databases that has occurred in the last
decade. The Department of Defense (DoD) collects DNA from all troops to identify
remains, police departments collect DNA from suspects and convicted criminals, the
federal government collects DNA, Iceland maintains a DNA databank of its entire
population, and Latvia has its own genome project. Databases of human DNA, sort
of personalized bar codes, have become ubiquitous in the modern age for managing

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populations for crime, health, and science. Foucault argues a surveillance system
“"compares, differentiates, hierarchizes, homogenizes, excludes. In short, it
normalizes" (1978:183). Foucault was specifically concerned about the relationship
between knowledge and power, how a state came to know its citizens. Nelkin and
Andrews (2003) claim there is much more involved the collection DNA samples that
just identification. Employers and insurance companies can find out information
about health and predispositions to disease, which can lead to discrimination, the
reproduction of racial and ethnic stereotypes, and the invasion of personal privacy.
In law enforcement, DNA is a special form of surveillance. Its counterparts,
fingerprinting and closed circuit television, are technologies of the past and present
respectively. Fingerprinting records a history of criminals and suspects and CCTVs
record events as they happen. DNA, however, is a technology of the future. While
DNA can act as a genetic fingerprint, law enforcement collects DNA to protect
against the mitigation of risk in the future. The function of individual nucleotides
that make up a strand of DNA is not well known and the range of information that
may be contained in a sample is still being discovered. At this point mapping and
sequencing of DNA is used in forensics for the purposes of identification. The 1994
DNA Identification Act and the Omnibus Crime Control Act authorized the FBI to
create the national Combined DNA Index System (CODIS), a networked database
system that links DNA databanks from all 50 states, the US Army Crime Lab, and
Puerto Rico (Cho and Sankar 2004). Each state has its own regulations for who is
included in the system. Some only include convicted felons while others include
misdemeanors. Louisiana, Texas, and Virginia collect samples from everyone who is

87
arrested, whether they are convicted or not (Ossorio 2005). By April 2004, the
CODIS databank contained 1,762,005 samples, including 80,302 from crime-scenes
and 1,681,703 from convicted offenders (Cho and Sankar 2004:S10). One of the
issues in regards to the samples that populate these databanks is overrepresentation
of minorities as the collections are biased against those who are already touched by
the criminal system. With known practices of racial profiling and a focus on certain
types of crimes, such as street crimes, racial bias in law enforcement means that
racial minorities, especially African Americans and Latinos will be subject to a
disproportionate intensity of surveillance (Duster 2004; Ossorio 2005). Further, the
predictive capabilities of DNA are only beginning to emerge, linking identity and
behavior.
Evans and Relling, leaders in the field of pharmacogenomics (how an
individual’s genetics affects the body’s response to drugs) published in Science the
statement, “All pharmacogenetic polymorphisms studied to date differ in frequency
among ethnic and racial groups,” followed by, “marked racial and ethnic diversity…
dictates that race be considered in studies aimed at discovering whether specific
genotypes or phenotypes are associated with disease risk or drug toxicity”
(1999:488). Here, DNA information moves from ethnic and racial identification to
ethnic differentiation of disease, sliding into gene behavior. Duster remarks that
subsequent correlation data generated by behavioral genetics would assuredly follow
“in an attempt to link such behaviors to violence, impulsivity and crime – and
lurking in the background – race” (2003:148). Just over two years after Even and
Relling’s claim in one of the most reputable science journals in the world, the same

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journal published initial evidence from Caspi et al (2002) stating a polymorphism in
the MAOA gene “moderates the impact of early childhood maltreatment on the
development of antisocial behaviors in males” (2002:853). This research suggests,
with low correlations, first, a connection between genes and behavior and, second,
drug intervention at the molecular level can ‘treat’ people who have been
“maltreated” as children and prevent them from becoming “antisocial” which is
leading towards criminality. Correlation does not mean causation, however. There is
no suggestion of the environmental factors and social dynamics at play in someone
getting into trouble with the law. As mentioned above, individuals from minority
groups are under high levels of surveillance, which attribute to the disproportionate
incarceration rates of African Americans and Latinos.
A number of scholars are concerned about how the functions of DNA
databases will “creep” beyond their mandates. Nelkin and Andrews (2003) describe
how DNA fingerprint tests have been used to assist governments in screening
immigrants. In 1989, the Thatcher government began using the test on prospective
immigrants to prove they had family in Britain. Canada began using this practice in
1991. Due to the origin countries of the immigrants and the costs of the tests, this
practice has been called discriminatory and used to discourage migration from
countries of the global south. At the same time in the US, pilot programs developed
worker ID cards that would carry, among other information, DNA sequences to
ensure that only legal aliens could hold jobs. With reduced cost of genetic testing,
concerns about a surveillance creep were realized when a senior member of the
British Police called for the entire population to be entered into a national DNA

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database. The DoD DNA database contains a full range of genetic information, not
just a DNA sequence. Garfinkel (2000) warns that a “mission creep” is likely as the
DoD could release the information for health and scientific research.
Cho and Sankar (2003) raise concerns about the “function creep” (S10) of
racially profiling of DNA in forensics. In the 1990s, a series of scientific papers from
forensics argued that race is coded in a number of sections of DNA (Devlin and
Risch 1992; Evett et al 1993; Evett et al 1996; Lowe et al 2001). When scientists
make claims about the relationship between race and DNA, they usually examine
variation between groups at a number of sections or loci. The series of papers
identify five racial/ethnic groups, Caucasian, Afro-Caribbean, Indian sub-
continental, Southeast Asian and Middle Eastern, used in the British law
enforcement databases. There they look for the frequency of an allele at that locus
for a specific population. The adequate number of locations varied through the 1990s
from three to fifteen. The markers for variation, called short-tandem-repeats (STRs),
were initially developed for analysis to identify or non-identify a suspect’s DNA
sample with one from a crime scene. However, Cho and Sankar explain how the
same information is being used to “create suspects where there are none”
(2003:S10). Some forensic scientists claim that certain STRs are associated with
racialized phenotypes, like skin color. So, instead of matching samples, this type of
analysis creates suspects, such as in a Louisiana serial killer case where the police
were searching for a ‘white’ suspect, but a company that markets such techniques
called DNAPrint, suggested that the suspect was actually African American. This
type of analysis elides the fluid and contested nature of race. Also, it reduces race to

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static notions of identity, which exclude the lived experience of racial difference. In
spite of the problematic assumptions of racial profiling of DNA and questions about
the connections between DNA and race (often assessed by what a police officer
observes rather than self report) genotyping of race continues, as does the expansion
of CODIS databases. While the FBI maintains DNA databases for security, the NIH
has been building them for health research.
The NIH launched Genbank, the first DNA database to collect and annotate
all publicly available DNA sequences, in 1982. This particular database has become
important in biomedical research not only as a resource but as a way of encouraging
scientists to make their data public. Many journals require submission to a database
before authors can submit articles. Sequencing of DNA was slow in the early days.
After four years, Genbank had only 680,000 base pairs and by 1986 that number had
grown to almost 10,000,000 (Moody 2004:26). Other difficulties included a backlog
of available sequences so that only a fraction could be entered and even those could
be years old. Now, Genbank holds approximately sixty billion bases (Genbank
2006). There are a number of other public databases at various institutes of the NIH
such as dbSNP, at the National Center for Biotechnology Information, which holds
Single Nucleotide Polymorphisms (SNPs). SNPs are locations of genetic variation
where people’s genomes differ by one nucleotide. For example, at the same position
on the genome one person may have a T base and another may have a C. SNPs have
been found to act as markers on chromosomes that assist in locating important genes
that could be involved in disease. With the turn to difference in genome research,
rates of SNPs have been used to measure variation between racial and ethnic groups.

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There is also the DNA Polymorphism Discovery Resource (National Human
Genome Research Institute) and Pharmgkb, the pharmacogenetics and
pharmacogenomics knowledge base, developed at Stanford University and funded by
the NIH. Globally, genome databases exist in Iceland, the UK, Switzerland, Japan,
and both Latvia and Estonia have their own genome projects (Kaiser 2002).

HapMap Databases and the Turn to Difference
One of the most important claims of the Human Genome Project (HGP) is that
humans are 99.9 percent the same at the genetic level. We all share the same genetic
makeup and are much more alike than different. This claim dates back to the work of
biologist Richard Lewontin in the 1960s.
6
However, the mapping of the human
genome by the public/private ventures of the National Institutes of Health and Celera
Genomics, headed by Francis Collins and Craig Venter respectively, has given the
claim authenticity. Also, with the media events that surrounded the announcement of
the completion of the first draft of the human genome in 2001, it is not only often
repeated in biomedical and scientific journals and by scientists, but has taken a place
in the public sphere. While the HGP found that human groups are all the same, the
HapMap Project is researching differences between groups.
The HapMap consortium collected samples from “populations with ancestry
from parts of Africa, Asia, and Europe” (International HapMap Consortium

6
While Lewontin has been credited with the 99.9 percent figure (Gannett 2001), the
discursive move to sameness was a prominent feature of the UNESCO Statements on
race (See Reardon 2005). I discuss terminology and slippage in concepts further in
chapter 5.

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2003:789). While project organizers deliberately decided to refer to the sample
groups in terms of “populations” and not racial groups, the initial groups do match a
traditional western taxonomy of race as well as the racial groups in the American
census. Lee (2005) points out that choosing such geographically disparate groups
accentuates any genetic distances/differences, rather than using more gradual
differences between more historically proximate groups. The gradual differences, she
argues, “might uproot conventional notions of racial boundaries and inspire new
trajectories of research that dispense with age-old notions of racial difference”
(2135). This reinforces the racial triangulation of black, white, and Asian. When the
National Human Genome Research Institute (NHGRI) of the National Institutes of
Health (NIH) decided to build databases and a haplotype map the scientists involved
had to make a decision on which markers to distinguish the data sets would be
included. The impetus for avoiding racial names can from the attendees who would
largely make up the ELSI committee.
it’s been an educational process for some, especially the genetic scientists,
but also for the people involved in all aspects. Some scientists were using…
East Asian or even Asian as a name for the Japanese and Han-Chinese
sample. We insisted that this is inappropriate as Asia includes too many
different groups, I mean thousands of different groups. China itself includes
fifty groups and you can’t call people like this. Therefore we adopted the
name JPT, and it was a long discussion and I think everyone had a kind of
contention on this in the end. I think the question of concern about racial
discrimination and racial discrimination and classifying these HapMap
samples in that way was a serious concern for everyone involved. That was
the reason for the care in taking the naming of the samples. Inside Japan, I
don’t think the concern is of any discrimination of Japanese people in Japan,
the concern for the Japanese people discrimination when the population
exists as a minority, for example in the Americas or Europe or Asia, other
Asian countries anywhere. (Interview 1011)

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HapMap organizers decided that the groups would be labeled according to
geographical rather than racial signifiers. I have been told that the discussions at the
preliminary HapMap meetings held in the summer of 2001 were very open about the
issue of racial identification. When asked about the place of race in the schedule of
items being discussed and whether or not it was an important issue or a marginal
one, a leading bioethicist in attendance commented that it was “in the fabric of the
meeting.” Another attendee described the discussions of race and community as at an
elementary level.
When interviewed, most HapMap members are quick to point out that they
do not use race but geographical ancestry to define the population groups. Not all the
members referred to the groups by geographic ancestry, however. A statistical
geneticist suggested equivalence between “the three sort of major continental groups
or racial groups or whatever your preferred term is” (Interview1002). The HapMap
sample populations are from different continents that closely resemble the main
racial categories in the US census and that are commonly used in biomedical
research. Risch (2002) and Petsko (2004) are examples of scientists who advocate
for using the racial categories set out by the Office of Management and Budget in
1977 (See chapter 4). When the NIH decided to build databases and a haplotype map
the scientists and bioethicists involved had to choose which markers would be
included. Should they include geographical location or not? They only decided that
racial names should not be included. Attendees to the planning meeting who ended
up on the ELSI committee pushed for the labels for the groups to be geo-markers.
However, it would be remiss to overlook the significance of choosing the three major

94
racial groups, African, Asian, and European that have largely defined the social and
scientific construction of race for centuries. Lee raises a more important question, “if
there is no genetic basis for race, then why do large scale mapping projects continue
to use racial categories in identifying research populations?” (2005:2135).
The answer lies partly in the tradition of population genetics and physical
anthropology that provides the scientific basis for choosing the four groups. Clearly,
there was an opportunity lost in debunking the relationship between genes and race
by choosing populations that are geographically distant from one another, rather than
ones closer together genetically and geographically. In addition, and this is an
equally important issue for identity, there are socio-political opportunities for states
who participate in large-scale genome projects. China and Japan have been eager to
assert themselves not only regionally, as leading countries geopolitically in Asia, but
on the world stage. Nigeria occupies a similar position in Africa. HapMap organizers
chose
…groups that were either already being engaged in some genetic research or
they were groups that researchers had already a relationship or a
collaboration with these groups or with persons in those communities… They
had contacts. They re-consented most of the people who were alive or whose
families were part of… CEPH. With the other populations, the Chinese
population and the Japanese, those populations, the specific populations, the
groups that were targeted, well I shouldn’t say targeted, were identified and
participated, were really groups where there was some contact or some
existing collaboration with those groups, for some researchers who was doing
some research for that group or that community or a collaborator in that
community. (Interview 1022)

Choosing the research sites and initial sample populations in the case of HapMap
was a matter of convenience and accessibility (Duster 2005). This is a common
occurrence across the natural and social sciences. As the dominant organizing

95
principle in the information age is the network, power operates through the space of
flows. In the case of HapMap, flows of scientific knowledge and social/professional
networks are accessible to scholars and states who can tap into them. In the early
1990s, Gilbert (1999) warned that the proliferation of databases would create a
digital divide: “The next tenfold increase in the amount of information in the
databases will divide the world into haves and have-nots, unless each of us connects
to that information and learns how to sift through it for the parts we need” (Gilbert in
Lenoir 1999:18). Cambodia or Vietnam were not ‘chosen’ or ‘targeted’ to represent
Asia nor was Sierra Leone for Africa, for example, because HapMap organizers did
not have relationships with anyone there and state governments lack the funds or the
international political capital to participate in the new global venture. However, some
members see the data coming out of HapMap being able to overcome this kind of
digital divide.
I think it’s an opportunity for the West and the industrialized economies to
efficiently transfer the intellectual benefits of wealth and investment and this
technology to the developing world. There’s no reason that South Africa has
to re-sequence the human genome to study the parts that are relevant to urban
disease there, they can leverage off of what we’ve done internationally. So, I
think that’s a fantastic opportunity for international science and humanities as
a whole. I think science can bridge boundaries in a way that other cultural
enterprises can’t do. (Interview 1016)

The HapMap database will expand with samples from more populations in latter
phases of the project. Phase II of the project has genotyped more SNPs from the
original four groups. There are future plans to sample more groups, but they are yet
unnamed.

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The Role of the Internet in Networking Knowledge Production, Open Access to
Data, and Informed Consent

I don’t think [HapMap] could have happened without the Internet, honestly.
(Interview 1016)
The impact of the Internet on biomedical research in general and the HapMap project
in particular has a number of facets. Broad changes can be observed in network and
distribution, form and knowledge creation, and ownership of data. The Internet is a
crucial form of communication for a global project with the geographical, technical,
and data intensive scope of HapMap. Many of the HapMap members expressed the
fundamental role of the Internet in facilitating a number of capacities. The four data
collection locations and the multiple sites in six countries were networked through
the Internet, making possible data distribution and retrieval as well as collaborative
communication between scientists. The project followed an open access model to
data ownership and distribution. Finally, this section will discuss how access to the
Internet has also empowered everyday people in negotiating the terms of consent in
biomedical research and practice.

Networking and Distribution
The Internet has transformed scientific research in terms of the networking of
scientists and information. Many of the interviewees cited information sharing,
online communication, and online collaboration as the main areas where the Internet
has impacted their work and made possible massive genome projects such as
HapMap. The Internet enables genome projects to move data between global
locations and labs in the same building as well as provide open access from anyone

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interested in using the information. The genetic information collected from Nigeria,
China, Japan, and Utah are sent to the participating labs located in six different
countries. When the sequencing has been performed, that data is uploaded to the
central HapMap database in Bethesda, Maryland, where it is curated, checked for
quality control, and stored. Since the project follows an open access model, the data
can be downloaded by anyone who has broadband Internet. Scientists can, in turn,
submit their own annotations for publication with the data on the HapMap website.
Distributing the enormous amounts of data that are produced from genomic
research used to be a serious problem, even in the earlier days of the Internet.
Different commentators have suggested what a paper based system of collecting and
moving around data would look like. In the early days of the Human Genome Project
and the nascent days of DNA sequencing, renowned Harvard biologist Walter
Gilbert speculated that a copy of the genome would be the size of “1000 1000-page
phone books” (1992:84). Gina Smith (2005), writing after the completion of the
Human Genome Project, thought that it may be “about 200 New York City phone
books worth of As, Cs, T,s and Gs” (2005:3). A top international geneticist
explained, without digital media and the Internet, distributing the data would be like
sending around “seven copies of the New York phone book” on a regular basis. “The
impact would be trivial compared to what it is.”
The Internet and digital media is not only necessary for moving the
information around, but also structuring the data itself. Digital technologies allow for
constant updating, cleaning, and translation:

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I had to imagine a model where all of this was done on paper. I don’t think a
system could comport itself because number one you’d always be
transcribing something, you’d be bound by the limitations of whatever media
you’re stuck putting the content on. So it’s just inefficiency upon inefficiency
and every time you’d have to move data from one media to another there’s an
opportunity for error. So just data correction and tracking errors and finding
out what’s in sync and what isn’t in sync, which could be a nightmare.
(Interview 1016)

Unlike the phone book, which can only be read in a very linear manner, the Internet
and digital media allows for the embedding of numerous supporting sorts of
information.
…a DNA sequence of 3 billion base pairs is cool, but an annotated DNA
sequence of 3 billion base pairs with take your pick of 22 thousand or 30
thousand genes is together with lots of regulatory elements and lots of
information in terms of where the polymorphisms and in particular the
coating polymorphisms non-synonymous ones and spliced variants and all
those different things. The ability not just to identify these different bits of
interesting information, annotation about the sequence but also to integrate
them in a useful way and in a very easily queryable set of databases is an
incredible advantage. Perhaps a more prosaic level perhaps just our ability
now to generate, to query databases about literature of all different sorts, you
know rather than going to the library and sitting around and looking for
things in the stacks, you know finding everything we want at a touch of a
keyboard in very short time is another huge advantage and then being able to
do searches and queries through those sorts of data is just a fantastic thing.
(Interview 1002)

The computer, database, and Internet facilitate the sharing of information in many
different ways. One of the more powerful features of an electronic database is the
cross referencing of information, which resides in different databases,
simultaneously, enabling very complex searches to be done on huge amounts of
information and complex analysis algorithms to be run easily (Interview 1009). Like
the anti-narrative logic of Manovich’s database-like films, genome databases can tell
different stories depending on the needs of the individual scientists. In turn, the

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outcome of a scientists work on a particular chapter of the ‘book of life’ can be
uploaded back into the database, thus changing the detail and scope of the original.
If you had not had a means of sharing that electronically it would have been
impossible to communicate any other way because the data is changing on a
daily basis. It is being added to, it is being refined, it's being developed, the
interpretations, the mistakes are being corrected and so on. You could not do
that if you didn't have a way of sharing that electronically. I mean you'd have
to fell the whole of the Canadian pine forest to make the paper on a daily
basis. (Interview 1007)

The HapMap database is a living, growing entity built from the original genomic
data from the project and subsequent additions from scientists around the globe. The
overall narratives are evolving in the sense that the mechanism of user feedback
constantly pushes the discursive possibilities and actual boundaries of the HapMap
genomic database in real time.
Further, HapMap does not operate in isolation from other sources of genome
data. The DNA information can be combined with other public genomic databases.
Originally, the database was governed by a click-wrap, licensing agreement as an
“interim protective strategy” before being completely released into the public
domain at the end of the project (International HapMap Consortium 2004:474).
Users of the genetic information could not combine the data with other DNA data
sets or, more importantly, patent it and prevent others from free access to the
information. As mentioned above, HapMap organizers did not want to completely
nullify the patenting process. The goal of the project is to make available detailed
DNA information that would enable researchers to link a specific disease to a
haplotype region and develop a pharmaceutical treatment. In December 2004, the
click-wrap agreement was dropped (NIH News Release 2004). This occurred earlier

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than originally planned because the initial agreement’s restricted use clause
prevented researchers from utilizing the information in combination with other NIH
databases.
Through the digital storing of genetic information, the Internet and related
technologies change the form of the data and the process of knowledge creation.
Again, the telephone book as a repository of information is a useful way to
conceptualize the impact of the Internet. DNA data in phonebook-like form is in an
order that is a series of letters. This is not really interpretable in any real way. With
the development of computers and the Internet, the same underlying data can have
many different representations.
So you can have representations that say I want to see what the genes are
across this region. You get some picture of here are the genes. But someone
else might come along and say, well I want to see how the genes vary in their
sequence. You get a different view of them. You say I want to see which
one's are expressed somewhere. There are all these different views of the data
and so without changing the underlying data you can customize to each user
the particular feature they're interested in. So it’s profoundly different than
having the same data in a phonebook. (Interview 1001)

The shared master version is constantly updated, instead of the changes from
individual teams or individual labs storing their changes locally, waiting for the next
round of data transfer. One of the main challenges for scientists has always been
access to the conduits of the Internet. In the nascent stages of the networking
technology, scientists transferring data on the few nodes of the system had to take
turns with limited university computing resources. This problem persisted with in
terms of storage disk space so that when there were more computers online, users

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could ‘borrow’ disk space from others in the network. A recent technological
innovation built to overcome the problem of disk space, bandwidth, and computing
power is the Grid.
The Grid has been called the future of scientific computing (Butler 2003).
The technology builds on the model of computing where computers on a network
would share their storage capacity. The Grid takes this architecture to open up
computing power, speed, and functionality in the same way that the World Wide
Web did for content. Once the Grid is given a particular job, its bundle of programs
goes off in the network and finds data and information and the computer space that is
needed to perform the task. Computer power is maximized through a network of
computers that may include a supercomputer or hundreds of smaller PCs logged into
the network. The “resource bookkeeper” keeps real time information on what is
available and what is happening on the Grid. With the massive amounts of data being
produced in the terabytes neighborhood (1024 gigabytes), the bounds of local
computing are again being pushed. While innovations like the Internet and the Grid
transform the technological possibilities of scientific research, they also change the
way scientists think about the culture of research.
Increasingly, argue Grid enthusiasts, scientists will see themselves less as
belonging to individual bricks-and-mortar institutions, and more as members
of ‘virtual organizations’, communities of researchers in defined research
areas or associated with particular experiments, who together decide what
computing and data resources they will share over the Grid. Gone will be the
logjams caused by limitations in computing power and data storage at one
institution, and the need to rack up endless frequent-flyer miles to participate
effectively in a project. (Butler 2003:800)

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Interestingly, one interviewee commented that his phone calls with collaborators had
decreased and his overall communication has increased with the diffusion of the
Internet. Other interviewees made similar comments about how the Internet
facilitated communication and collaborations. These processes are fundamental to
scientific research today as evidenced by global genome studies such as HapMap,
which can operate on a 24-hour basis with research sites scattered across the world.
HapMap members suggested that writing papers has become easier with electronic
mail (Interview 1005), the Internet makes it possible for international ventures to
work together in real time (Interview 1007) and with greater quantities of data
(Interview 1011). Like many other segments of society, cyberspace has clearly
impacted the manner in which biologists work with one another and, as the next
section will show, how the storage of genome data has become a public issue.

Genome Data as a Public Good: Democratizing the Data Through the Public
Domain

Gilbert’s warnings about the digital divide of information haves and have nots
amongst scientists and nations globally have become partially true. As projects like
HapMap show, those who come to the genome table are mostly from developed
countries, mostly from the west, but at least regional powers. However, one of the
mandates of the project is that the information contained in the database will be
“freely available in the public domain, at no cost to users”
(http://www.hapmap.org/cgi-perl/registration). The HapMap Project follows an
immediate data access approach by making the findings available publicly on the

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Internet within twenty-four hours of the data being collected from the multiple sites.
The open access model represents a movement in the academic and public sector
scientific community. This type of approach goes against the traditional practice of
scientists in keeping their data private until publication and the proprietary nature of
the biotechnology industry (Collins 2006). The open access model originated at a
1996 meeting between British and American scientists in Bermuda, chosen for its
neutral location. The attendees decided that DNA data should be made available
freely, in a timely manner, and protected from copyright (Shreeve 2004:46). The
resulting guidelines have come to be known as the “Bermuda Principles.” Within
HapMap, however, there were heated discussions about the open access mandate and
the ability to patent the genetic information (Interview 1017).
The Internet provides the means for this mandate to be carried out. Like other
battles over intellectual property and copyright, such as the downloading of music
and the Napster case, human DNA has virtually unlimited opportunities for
developments of cures for disease. Since the 1980s, the philosophy and practice of
the Internet has been between copyright and the open source idea of copyleft, where
programs are released free over the Internet with the intention that any improvements
made by users would, in turn, be released back onto the web. The most famous of the
open source model is Linus Torvalds’s Linux operating system. HapMap members
are particularly enthusiastic and principled about this practice (Interview 1016):
If you look at some of the databases now that are open to all researchers that
constitute international resources, the collaboration used to be one on one in
terms of disease or in terms of friendship even between different scientists.
Now we have international databases that are curated, annotated, put up to
date where people can share. So in terms of making science really

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international and making science open in the humanistic, old sense of
science, open as in belonging to the public, I think its been absolutely
tremendous. (Interview 1008)

A number of the participants of the project referred to this process as “democratizing
the data.” There are a number of features to open access. The information needs to be
accessible through files that can be downloaded from a public website. Of course, the
user needs to have access to the Internet and, ideally, a broadband connection as well
as the facilities to store the data. In the case of HapMap, the project’s homepage
serves as the portal to the project databases. Simple queries that return responses and
graphical interfaces for browsing data are critical to the sharing of data. One
respondent felt that, in the long run, this approach will have a “great and profound
impact on the way biomedical science is being done because it’s a very infectious
idea and it’s not and idea that existed in biomedicine before” (Interview 1001).
Biomedical research has been a process of doing one’s own experiment, writing the
result in a notebook, which sits there until the publication of the results that may only
share parts of the primary data. Prior to the Internet, the data would be held locally as
there was no pressure, technologically or in the scientific research culture, to freely
distribute it as “ researchers enjoyed a luxury of primary access and unique access to
their data” (Interview 1016). A scientist would share the information with colleagues
by presenting the work at a conference or writing a paper that goes through peer
review. Either way, the end result is a highly extracted and interpreted diversion of
an experiment. That paradigm is evolving due to research like genomics and the use
of ICTs.

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When you collect the data using a more objective device that sort of collects
the raw data and then you share it over the Internet the next person can come
along and do a different interpretation of that data. And that is totally, that is
dependent on technologies for collecting data. But it's, it's most
fundamentally about archiving and distributing data, which is based on the
Internet. (Interview 1001)

Democratizing the data depends on the network capacity of databases and the
Internet as well as a social movement from within the biomedical sciences. It appears
to directly confront private models of the biotechnology industry where the keeping
of trade secrets in closed labs is considered crucial to competing in the marketplace.
Organizers of HapMap are not against the practice of patenting. The HapMap
“Data Release Policy” states:
All data generated by the Project will be released into the public domain. The
participants in the Project believe that patents should not be issued for a SNP
or haplotype for which a "specific utility" -- as defined in patent law -- has
not been generated. However, if a specific utility can be demonstrated for a
SNP or haplotype, any group, whether associated with the Project or not,
should be able to apply for a patent, as long as this action does not prevent
others from obtaining access to data from the Project.
(http://www.hapmap.org/datareleasepolicy.html)

Democratizing the data works in terms of access, however, one HapMap member
pointed out the limitations of this approach in terms of the quality of data. Differing
from the copyleft movement and open source, where the codes can be redesigned
and released into cyberspace, HapMap data has curators to monitor data quality. The
job of curator takes place at the National Center for Biotechnology Information at the
NIH’s National Library of Medicine and works as a kind of gatekeeper of the
information, not in terms of access, but of the actual content.
The problem with data is that not all, in a democracy all voices of reason
should have an equal opportunity to be voted and heard. There is a tension in
science between having all possible voices heard and in running the risk of

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some of those are not accurate, meaning that you know we do need a way of
establishing standards of quality and content and then being you know, either
saying you know the stuff we distribute is known to be of high quality and we
don’t need to individually measure the quality of every datum as it moves
through a plate. Or we need a transport mechanism that accepts everything
but allows a user to filter easily for high or low quality at every step. You see
that redundancy is good when everything’s being measured well, but a
project that just introduces a lot of noise and low quality data into a system
could probably be, everyone’s worse off, it’s a tragedy of the commons kind
of effect. (Interview 1016)

Marturano (2003) explores the notion that molecular biologists can be understood as
hackers of human data. He suggests that scientists should adopt the open source
philosophy followed by many computer hackers where the source codes are shared,
modified, and redistributed. This would strengthen the scientific community and
refocus the emerging patent-and-perish culture to a gift economy where status among
peers comes from the sharing of knowledge, which is already part of the practice of
scientists. Ultimately, an open source philosophy seeks to protect genomic data as a
public good, rather than something that can be owned by a corporation. The
immediate release into the public domain approach could have far reaching affects in
terms of the divide between information haves and have nots.

Techno-Consent and Community Engagement
The social movements of the 1960s and 1970s challenged existing norms about the
order of society and, primarily, the subordinate positions of racial minorities,
women, and gay men and lesbians. One set of the institutions that were especially
targeted by the feminist and gay liberation movements was the biomedical sciences.
Science and medicine had long kept a distance from the public with legitimacy,

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credibility, and expertise exercised from within. Scientists and doctors talked and
everyday people listened with a sense of awe. Trust in doctors was automatically
assumed. This arrangement became disrupted from the 1960s to the 1980s. Radical
critiques of the medical-industrial complex in the 1960s were joined by women
working to regain control over their own bodies in the 1970s, which was built on by
AIDS activists working with and inside biomedical institutions in the 1980s (Epstein
1996). Issues such as patient or subject participation and trust moved from being
assumed though the authority of the doctor or scientists to informed consent being
earned through ethical regulations and conversation between the two parties.
Ordinary people have become much more involved in the health process in
understanding their personal health issues.
An internationally renowned bioethicist and biologist explained that the
older, paternalistic models do not work in the information age (Interview 1011). This
is largely due to the social movements descried by Epstein, but also due to the
diffusion of ICTs. In top-down approaches to scientific research, samples were
simply taken from individuals without being informed of the risks or outcomes. This
is no longer ethically acceptable as subjects must give their consent though an
informed process. Informed consent is negotiated at both the community and
individual levels. For example, the HapMap Project engaged the four participating
populations in community consent, which differs from traditional consent models in
two key ways. First, consent must be gained at the local level through representative
community groups. Second, the terms of consent are not simply presented by
researchers and signed by individuals. Consent is negotiated between the researchers

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and community representatives. Organizers of the Human Genome Diversity Project
(HGDP) encountered massive resistance from numerous indigenous groups when
approached using the traditional top-down approach. The HGDP was nicknamed the
“Vampire Project” as community groups recalled a history of mistreatment by
western scientists and protested the seeking of the blood of indigenous people with
no clear outcome of improved health (Reardon 2005). Native American
representatives from Indigenous People’s Against Biocolonialism attended the initial
HapMap meetings in the summer of 2001 and declined to participate until the
research showed that their inclusion was necessary for scientific reasons (Interview
1022).
At the individual level, people are able to make better-informed decisions
about participating in research and their own health situations. The international
bioethicist referred to this emerging phenomenon as a “choice model” and linked its
development to the rise of information technologies.
…this transition is clearly happening in every society around the world at
different speeds, different rates, and different ways… information technology
is behind that. The development of personal computers, access to the
Internet, new forms of communication… I think even new parts of personal
identity is represented in information technology, is clear for people, they
spend increasing amounts of their daily life involved in communication, on
mobile telephones, email, and other forms of communication. (Interview
1011)

Information technologies, such as the Internet, have been important in creating a
“leveling effect” between different groups in society. Information about health and
scientific studies has become much more accessible to everyday people. A consistent
trend in rankings of top Internet usages since the mid 1990s has been for finding

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health information (Cline and Haynes 2001; Cole 2004; Leaffer 2006; PEW 2005).
People commonly use the information to ask new questions or a second opinion
(Williams-Jones 2003). While ICTs have been key in making people more informed,
some argue that there are ways in which ICTs may be threatening informed consent.
Tavani (2004) argues that the same technologies that have made it possible to
find genes that cause certain diseases which speeds up the process of discovering
cures are undermining the principle of informed consent by threatening research
subject’s privacy. The route to personalized medicine is currently through the
studying of group differences in proclivity to disease and reaction to treatments.
Hunting for disease genes produces huge amounts of data that is stored in databases.
This data is sorted through and analyzed using data mining techniques which
‘discover’ hidden patterns, properties, and statistical correlations. New aggregates of
groups or categories can be produced in the process. These types of community
formation “make up new citizens” through biomedical and biological languages and
practices combined with everyday people organizing along lines of disease
knowledge and prevention (Rose and Novas 2004:445). While subjects contribute
their DNA to health research, they are unwittingly contributing to this process. The
creation of a controversial group could produce stigmatization. This was an ethical
issue clearly identified by HapMap interviewees (Interview 1011). While the Internet
has made the information about diseases, treatment, and other areas of health more
transparent to the public, Tavani suggests that databases are making informed
consent more opaque.

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Conclusion
Innovations in information and communication technologies have been intimately
tied to the biological revolution since the discovery of rDNA made possible the
genetic engineering of life. Since that key discovery in the early 1970s and the
discussions at the Asilomar Conference, a new type of biology based on
computational techniques has emerged and joined the wet labs of experimental
biology. In the process, biology has incorporated theoretical and practical aspects of
computing, becoming an informational science. The convergence of the biological
and electronic revolutions has been fundamental to the new science of genomics.
Genome projects such as the Human Genome Project and the International HapMap
Project have not only incorporated the technological and scientific transformations,
but also motivated them.
Two of the key technologies that are having major impacts on genomic and
the production of scientific knowledge are databases and the Internet. The massive
amounts of information that are created from genome mapping and sequencing are
stored, sorted, analyzed, and networked in DNA databases. Other sectors of society
such as commerce and law enforcement are building databases for the surveillance of
populations, such the FBI’s CODIS database. The Internet is the conduit for the
transfer of genome information. It also networks labs, scientists, and international
projects, such as HapMap. Scientists and bioethicists have also been utilizing open
access models to “democratize the data” and keep DNA information in the public
domain. While open access attempts to keep DNA data public rather than
proprietary, everyday people use communication technologies to challenge the

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traditional paternal models in biomedical research and clinical practice. Access to
information has been key in this “leveling effect.” Through the Internet patients and
research subjects can gain information on research projects and their health care
needs. However, ICTs such as databases are also increasing surveillance and
threatening the privacy of subjects, patients, and anyone else caught in the
informational net of data gathering.
The next chapter will turn to the institutional and legal changes that have
transformed the sciences, biomedical research, and genomics from 1977 to 2004. A
series of government policies and legal cases since the 1970s focused on
deregulating the relationships between commerce and universities. Contrary to the
fears of the scientists at Asilomar in 1975 about outside control over the methods and
conditions of biology, the federal government has facilitated the industrialization of
biological research in an effort to make the US a world leader in biotechnology. This
produced a climate where some scientists became weary of research submitted to
journals that was increasingly being funded by corporate interests. Journal editorials,
comments, and letters to the editor become forums for discussing conflict of interest
policies. Finally, another set of institutional changes also occurred in the same
journals as well as government organizations such as the National Institutes of
Health that were aimed at the inclusion and treatment of ethnic and racial minorities
in biomedical and scientific research.

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Chapter 4
The Legal and Institutional Formation of Biotechnology and Genomics, 1977-
2004

The informationalization of race has emerged as new communication technologies
evolved since the 1970s and become integrated across social institutions and as
society has undergone transformations in the organization of race. As this
dissertation argues, the informationalization of race is not only due to the social
shaping of new technologies, but also the transformation of regulatory and cultural
infrastructures. While the previous chapter showed the technological and
organizational changes that occurred in biology and genomics, this chapter focuses
on what has become one of the most controversial topics in the development of
modern societies, regulation. The HapMap Project in particular and biomedical
research more generally has been formed not only by scientific, politico-cultural, and
technological developments, but a series of regulatory changes from the courts to the
senate to the editorial boards of scientific and medical journals. This chapter shows
how genome research has been formed, in part, by changes in legal and institutional
structures and mapping the genealogy of changes is central to understanding the
ethical and legal infrastructure that under girds HapMap. There are two different
trajectories of regulation that I address in this chapter. The first is a common one in
the constellation of neo-liberal policies enacted by government administrations in US
and elsewhere as capitalism underwent restructuring in the 1980s. In order for the
United States to become a world leader in the nascent field of biotechnology, the

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Reagan government needed to create the conditions that would allow and encourage
interaction between private industry and academic labs. The early 1980s was a
period of deregulation in the biotechnology industry. The second trajectory concerns
the regulation of racial identity. As diversity and multiculturalism became goals
across institutions in the post-civil rights era, biomedical models of research
underwent significant transformations in terms of inclusion of women and visible
minorities. In this chapter, I show how biomedical research has been formed by color
conscious policies from the Office of Management and Budget to the National
Institutes of Health to the editorial policies of scientific and biomedical journals. I
begin this chapter by discussing the role of regulation in the field of communication.
Regulation is an important object of study in the field of communication.
Communication scholars have made important contributions to broad areas of media
research such as policy analysis of the telecommunication industries around the
world (Galperin 2004), the deregulation of media industries in the United States
(Kellner 1990; McChesney 1993, 1999), and the emerging area of wifi networks
(Bar and Galperin 2004, 2005; Galperin 2005; Park 2007; Park and Bar 2006). The
deregulation of media and telecommunications in the United States has been a
crucial trajectory followed by media scholars. From the enactment of the 1984 Cable
Act to the 1996 Telecommunications Act under President Clinton, various media
industries were opened up for convergence and conglomeration as decades old anti-
monopoly laws were struck down in favor of neo-liberal economic policies. While
the Reagan administration began to deregulate decades old FCC rules governing
media and telecommunications industries, they were also writing laws to facilitate

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the burgeoning biotechnology industry. At stake in biotech are not only interventions
into human identity at the genetic level, thus altering the molecular material that
constitutes our physical, emotional, and cognitive selves, but the symbolics of the
body: “biotechnologies are disrupting some of what are our most fundamental social
categories and boundaries” (Gerlach and Hamilton 2005:80). The deregulation of
biotechnology has not been a traditional sphere of analysis for media and
communication. Recently, however, there have been calls for communication
scholars to examine biotechnology’s impact on society. The February 2005 issue of
Communiction Theory was a special edition devoted to biotech. Cultural scholars in
the issue focus on representation in terms of media coverage of biotechnology,
identity and the body. Other papers examine the current legal debates about
ownership, patenting, and ethics of scientific research, such as cloning and stem cell
research.
While battles over media representations have been prominent in scholarly
literature and public discourse, representation through information and
communication technologies and biotechnology has focused mainly on futuristic
scenarios for stem cell research and cloning. Biotechnology is a representational
form and, unlike the media industry, it is a sector and system of knowledge
production that is in the early stages. The emerging “biomedia” (Thacker 2004)
warrants closer attention as a political economic process.
Some of the changes below have been discussed in the context of the
deregulation in the 1980s that fostered the biotechnology industry. Scholars of
university-industrial relations have paid particular attention to the industrialization of

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academic research and the influence that commercial funding exerts on academic
labs, science departments, and university administrations (Kenny 1986; Krimsky
1991, 1999, 2004; Kleinman 2003). Scholars of race have widely discussed the
OMB’s Directive 15, which set the current racial and ethnic categories for the U.S.
census (Wright 1997; Skerry 2000). Biomedical researchers have examined the
impact regulations aimed at diversity in bio-scientific studies have had on publicly
funded research (Brawley 1995; Epstein 2004). The above scholars have all
examined different parts of regulation and biotechnology. Even more include some
of these changes in mapping out the institutional context of emerging phenomena
such as the new drug targeted for African Americans with heart disease, BiDil
(Kahn, 2003, 2004). Almost all of the roads to regulation begin with the Baye-Dole
Act of 1980 (See below). Big science genome projects, such as the HapMap project,
have become characterized by the participation of public and private entities, which
is the result of the deregulation of university-industrial relations that began in the
early 1980s. Typical histories of sector policy changes would end there. However,
big science, at least the kind that is funded by the U.S. government through agencies
such as the National Institutes of Health and the National Science Foundation, has
been transformed not only by deregulation, but changes in how subjects are recruited
to be more inclusive and diverse in study populations and how major journals in the
field have developed editorial policies for disclosing financial conflicts of interest
and for using race and ethnicity in research protocols. This chapter seeks to connect
this web of legal and institutional changes and bring together significant legal and
institutional developments in the (re)regulation of biotechnology research.

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The Legal Signal for the Commercialization of Biotechnology: Diamond vs.
Chakrabarty

The discovery of recombinant DNA in 1973 marked the coming of age of biology.
Chemistry and physics had already been creating innovative technologies that could
be useful for society and made into commercial products for well over 100 years. It
was not until the ability to remove, splice, and transplant genes was possible that
molecular biology became an industrial tool. As Krimsky (1991a) states in his
historical account of the emergence of the biotechnology industry, even though the
ancestral roots of industrial microbiology date back a number of centuries, its rapid
and industrial growth occurred in the 1970s.
Each new scientific advancement became a media event designed to capture
investment confidence and public support. Market expectations and social
benefits of new products were frequently overstated. It was part of the
“geneticization” of the social mind. People were being prepared to see
genetics as the next great advance in technological progress. (Krimsky
1991a:21)

While the social was being primed, the institutional environment that constrained the
industry required overhauling. In 1980, a number of significant changes occurred in
law and government policy such as the patenting of a microorganism and the
beginning of the deregulation of university and industry relations that would provide
the institutional foundation for the biotechnology industry.
Ananda Chakrabarty was a scientist working for General Electric Company
when he made a discovery that would challenge centuries old patent laws and begin
the rapid pace of growth in the biotechnology industry. He developed a bacterium
that could break down the components of crude oil and filed a patent for the microbe,

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the process for making it, and the method of its distribution. The U.S. Patent and
Trademark Office (PTO) initially accepted the application, except for the patent for
the microbe itself. The PTO examiner objected stating that modifying life-forms
through genetic rearrangement does not create a product of manufacture. Only man-
made products are capable of private ownership by U.S. patent law that dated back to
1790. Chakrabarty’s microbe was deemed a product of nature and, hence, not a
matter that could be patented. Two higher patent courts, the Patent and Trademark
Office board of appeals and then the Court of Customs and Patent Appeals (CCPA),
reversed the initial decision on the grounds that the modified bacteria are in fact not
naturally occurring and qualify for a patent like other products of manufacture. The
CCPA added that being of living matter did not exclude such a product from
patenting. The case had its final hearing on June 16 when the Supreme Court upheld
the ruling of the CCPA by a 5-4 decision. Speaking on behalf of the majority, Chief
Justice Warren stated that new types of living matter are no different than lifeless
products and that the main distinction in patenting cases is whether or not the
products are man-made. The Supreme Court broadened the scope of patentable
material to “include anything under the sun that is made by man” (Krimsky
1991a:47-8).
There is disagreement on the significance of the Supreme Court’s decision on
the patenting of organisms for the biotechnology industry. Many consider this case a
watershed mark and see the issue of patent as crucial for propelling the industry
forward. Business representatives testifying at a Senate hearing on the industrial
application of rDNA in 1980 argued that patenting would be crucial to the growth of

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the industry. The threat here, of course, was that without patents biotechnical
knowledge would be forced into trade secrecy. Legal critics argued, however, that
the Chakrabarty case is less significant and can be considered “trivial law” as the
Supreme Court did not break legal ground. Either way, the importance of the case
lies in it indicating to entrepreneurs and scientists that this new technology was open
to commercialization (Kenny 1986). With the Chakrabarty case, the courts signaled
the rise of the biotechnology industry. The U.S. government was another body that
anticipated the potential of the industry and was quick to further facilitate its early
formation.

Deregulating University-Industrial Relations
From 1980 to 1986, the U.S. government introduced a number of acts, amendments,
executive orders, and memorandums to facilitate the growth of the biotechnology
industry, signaling the aim of the United States to become a global leader in
biotechnology. These policy changes were aimed at transforming relations and
building crucial relationships between university labs, industry, and government
agencies for the transfer of scientific knowledge and collaboration. President Carter
signed the first pair of bills in 1980, the Stevenson-Wydler Technology Transfer Act
and the Bay-Dole Patent and Trademark Laws Agreement. Stevenson-Wydler
encouraged collaborations between universities, government labs, and industry. The
goal was to convert scientific knowledge from the labs in to commercial products.
Baye-Dole, along with the Government Patent Policy Act (GPPA) of the same year,
gave university research institutions funded by the federal government property

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rights over their discoveries (Kahn 2004; Krimsky 1991a). Obligating universities to
apply for patents was aimed at protecting commercially valuable discoveries from
foreign competition before the information was released into the public domain
(Shreeve 2004). A 1983 presidential memorandum extended the GGPA from small
firms and universities to federally funded large businesses and contractors. The
GGPA was amended again, this time by Congress, in 1984. Companies were
awarded tax credits for investing in basic research in universities through the
Economic Recovery Tax Act of 1981 (Buctuanon 2001). President Reagan extended
the Baye-Dole Act to all industry by executive order in 1983 (Krimsky 1991a).
To close the circle of research partnerships among industry, universities and
government, Congress passed the Federal Technology Transfer Act of 1986,
which expanded science-industry collaboration to laboratories run by the
federal government. Governmental standards for keeping an arm’s length
from industry were being turned on their head. Through this act, a
government scientist could form a “Cooperative Research and Development
Agreement” (“CRADA”) with a company as a route to commercializing
discoveries made in a federal laboratory. Government scientists could accept
royalty income up to a given amount, fifteen percent of the National Institutes
of Health (the “NIH”) share, to supplement their salaries. At the time this
policy was enacted, there was virtually no public discussion about the blatant
conflicts of interest that this would introduce. The CRADA required
government scientists to keep company data confidential and impeded the
sharing of information in government laboratories. (Krimsky 1999:21-2)

The completion of the 1986 Federal Technology Transfer Act marked a
significant deregulation of constraints between industry, the academy, and
government agencies by encouraging federal laboratories to commercialize results
(Buctuanon 2001). The institutional environment for the biotechnology industry at
the end of the 1970s looked completely different by the end of the 1980s. Not only
had the amount of biotech startups increased dramatically by the hundreds (Krimsky

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et al 1991b:275), the number of patent applications from hospitals and universities
increased over 300 percent in the years 1980 to 1985 compared to 1975 to 1980
(OTA 1987). As the biotech industry grew, companies recruited university scientists.
Some scientists became entrepreneurs and began their own companies. This created
a crisis for scientific fields in terms of conflict of interest where private companies
funded basic research.

Conflict of Interest Policies in Biomedical and Science Journals
In the formative stage of the biotechnology where the regulatory changes outlined
above encouraged university-industry-government partnerships and collaborations,
there was a general concern about the how the changing norms, motivations, and
values of the new entrepreneurial scientist would affect scientific research and its
social benefits. Krimsky et al (1991b:276) suggest a number of possible negative
consequences such as conflicts of interest, shifting of research agendas from basic to
applied, and an erosion of the open model of communication between scientists in
favor of a corporate model based on secrecy. Kleinman (2003) has delineated
consequences into direct and indirect effects. A direct effect would be the disruption
of an open model of communication between scientists as a stipulation of external,
private funding. Direct effects have been the focus of much of the research and
media interest in the commercialization of university research. Indirect effects
suggest a more systemic change in the values, norms, and culture that structure
academic research. For example, the erosion of the free flow of information would
be much more subtle and pervasive than a contractual obligation to knowledge

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secrecy. Instead of an imposition from without to guard information, university
scientists begin to regulate their own conduct which signals a shift in academic
culture from a scholarly one to a corporate one. Scientists and scholars have been
concerned about the movement from the protected ivory tower to, what Krimsky
refers to as “academic enterprise zones” (2004:5).
The concerns over the development of university-industrial interactions in the
early 1980s coalesced around the issue of conflict of interest. Questions were raised
about the way that entrepreneurship would infect the pursuit of pure science and the
role of scientific research in the betterment of society. Simply put, instead of science
being in the interest of the social good, research aims would serve the interests of
private companies. The New England Journal of Medicine (NEJM) was the first
major medical journal to include commentary about conflict of interest and has been
a leader in setting ethical standards in publication. In a 1984 editorial, Relman
argued that “it is not possible for medical investigators to have their research
subsidized by business whose products they are studying, or act as paid consultants
for them, but they are sometimes also principles in those businesses or hold equity
interests in them (1182). Shortly thereafter, NEJM introduced a policy of conflict of
interest, becoming the first major medical journal to require authors indicate if any
part of their original research was privately funded or had any financial interest
(Krimsky 2004:166). Others journals in the biotechnology and biomedical fields
followed suit, but with varying speeds of inclusion and not without serious dispute
over the merit of such a policy.

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The leading scientific journal Nature was a notable holdout to a conflict of
interest policy. A 1997 editorial, entitled “Avoid financial ‘correctness,’” played on
the wave of conservative discourse in the 1990s against progressive social policies
and discursive changes in references to minorities and women, referred to as
‘political correctness’. The editorial argued that declaring business interests is
“beside the point” and that virtually every paper in biotechnology from the east and
west coasts of the U.S. and European laboratories “has at least one author with
financial interest” (Editorial 1997:469). In spite of the editorial acknowledging the
ubiquitousness of the industrialization of science, it states that the “measurements
and conclusions are in principle unaffected” and concludes “this journal will persist
in its stubborn that research as we publish it is indeed research, not business” (Ibid).
At the time, 16 percent of the top one thousand science and medical journals had
conflict of interest policies (Krimsky 2004:169). Nature refused to include such as
statement until 2001 when it adopted a “declaration of financial interest” policy
(751). Conflict of interest policies have become much more commonplace. The
leading genetics journal, Nature Genetics, requires a “competing interests
statement,” and Pharmacogenomics Journal calls theirs a “duality of interest.”
Science has similar requirements of authors. While concerns and criticisms about
conflicts of interest have been around for over two decades, it is only in the last few
years that conflict of interest policies have become the norm in biomedical and
biotechnology journals. In spite of widespread adoption, journals have been found to
be uneven in their compliance to their own conflict of interest policies (See Krimsky
2004; Krimsky and Rothenberg 2001).

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Color Consciousness in a Time of Colorblindness: OMB Directive 15, NIH
Revitalization Act of 1993, and Editorial Policies on Race and Ethnicity

While biomedical journals argued the merits of acknowledging the industrialization
of scientific research in conflict of interest statements, the scientific community was
also confronting ethical issues about diversity. Since the 1960s, women, sexual
minorities, and people of color have challenged the closed, hierarchical nature of the
biomedical sciences. For example, the doctor-patient relationship has changed from a
paternal model to a choice model where ordinary people have more access the health
information and, as a consequence, are becoming much more knowledgeable and
active about their personal health. Epstein (1996) refers to this transformation as a
“crisis of credibility.” Patients have access to varying types of health information due
to communication technologies such as the Internet. The feminist movement in the
1970s and AIDS activism in the 1980s challenged existing norms and forced outside
participation in the conduct of biomedical research. The underrepresentation of these
groups as well as racial minorities became a recognized social problem in the United
States in the 1990s (Epstein 2004). In terms of race and ethnicity, the mounting shift
to diversity as a social problem was address institutionally though the revision of
OMB Directive 15, the NIH Revitalization Act of 1993, and, journal policies on race.
Each is reflective of changing social and political norms about race and
representation as well as resulting in internal struggles over identity in the
biomedical sciences. The combination of these changes ushered in what Epstein
refers to as a “new common sense” in research policies and practices “that had

124
seemed uncontroversial and even ethically advisable suddenly began to appear
ludicrous, offensive, and unscientific” (2004:187). The taken for grantedness of the
white, male centered approach to the object of scientific and medical research was
disrupted by voices from the margins, women, gay men and lesbians, and minorities.
The new common sense refers to the inclusion of these groups and the
institutionalization of their voices and bodies. Instead of the tradition of
marginalization of women and people of color from the power structure of the
biomedical sciences and the omission of them as research subjects, the focus of the
new common sense became diversity.
As diversity and color consciousness became a focus of biomedical
institutions in the 1990s, the dominant racial ideology emerging in the wider society
was colorblindness. The color conscious programs that originated from the civil
rights movement came under serious attack not only from the right, but the political
center. At the same time, the US Office of Management and Budget, the NIH, and
various journals in the biomedical sciences created color conscious policies that were
in reaction to earlier social movements as well as setting the stage for a new wave of
scientific research. The institutional structures that enable and constrain the HapMap
Project, the way in which it recruits subjects, and the manner in which it frames its
questions around race is the result of changes in NIH policies aimed at diversity in
research subjects, revisions to the US census, and the creation of biomedical journals
editorial policies regarding the use of race in research. The next section of the
chapter will discuss the institutional changes that have shaped the ways that
scientists use race as a category in scientific research. In particular, this section will

125
discuss the OMB’s Directive 15 of 1977, the NIH Revitalization Act of 1993, and
journal policies on using and reporting on race in scientific research. All of these
changes in the regulatory landscape of institutionalized identity shape the ways in
which the genomics and the HapMap project define and use racial groups and recruit
research subjects.

OMB Directive 15
In 1977, the federal government’s Office of Management and Budget issued
Statistical Policy Directive 15 that set the standard for racial and ethnic identity for
the census. The OMB instituted four racial and two ethnic categories: American
Indian or Alaskan Native, Asian or Pacific Islander,

black, and white; Hispanic origin

and not of Hispanic origin. The racial groupings draw on Linneaus’s 300 year-old
taxonomies of race, lending weight to the existence of biologically distinct human
groups so entrenched in the popular imagination. His classification drew on the
image of geographically separate groups, Afer (African), Americanus (Native
American), Asiaticus, and Europaeus. Gould (1994) argues that Linnaeus’ groups are
not linear or hierarchical but cartographic. Donna Haraway suggests that
cartographic models are fetishized forms of troping that, on the surface, appear value
free but actually are steeped in colonial desire (Haraway 1997). The obvious mixing
of behavioral traits (humor and disposition) and physical features (color and posture)
in Linnaeus’s model are more reflective of European sentiments of the Other than
any objective standards. However, it is not Linneaus but his student, Blumenbach,
who is credited with the origins of racial classification. Blumenbach made two

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additions to the Linnaean system. He added a fifth group, the Malay, making a
geometric shift in the configuration of human groups, and ranked them according to
European ideals of beauty. Instead of the groups occupying the four corners of the
earth, the five-race system “radically changed the geometry of human order…
fanning out in two directions from a Caucasian ideal” (Gould 1994:66). Blumenbach
took the name Caucasian from, what he considered to be, the superior beauty of the
people from the Mount Caucasus region in Eastern Europe. Both Linneaus and
Blumenbach created their taxonomies during a time of European expansion and
colonialism, and American slavery. These systems of oppression were rationalized in
scientific race thinking, the biologizing of race, and scientific racism in the
nineteenth century. During the twentieth century in the United States, the racial
categories have been changed twenty-six times in the US census to categorize:
…those from the Indian subcontinent as ‘white,’ then ‘black’ and more
recently as ‘Asian Indian.’ The most recent addition of the categories of
‘Native Hawaiian,’ ‘Gumanian or Chamorro,’ ‘Samoan,’ ‘Other Pacific
Islander,’ and the miscellaneous class of ‘Some Other Race’ reveals these
categories a socio-political struggles for representation. (Lee 2003:388)

At different points, various European ethnic groups, such as Italians and Irish, were
considered distinct racial groups. Both groups have gained entrance into whiteness
and would now be subsumed under the category ‘white’.
7
Different racial

7
For historical analyses of the social construction of whiteness see Ignatiev (1995)
How the Irish Became White and Jacobson (1998) Whiteness of a Different Color:
European Immigrants and the Alchemy of Race. Many European ethnicities outside
of the dominant groups such as the English, French, and German were considered to
be lower ‘races.’ Ignatiev explores the case of the Irish and how they used labor
unions, the Catholic Church, and occupied an oppositional position to African
Americans in order to gain entrance into white America. Jacobson masterfully
explains how different European groups became white through three great racial

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designations have come and gone such as octoroon and quadroon in the 1790 census
to indicate shades of blackness (Epstein 2003; Goldberg 1993). Historically, ‘black’
has included anyone with one drop of ‘black’ blood. The census currently relies on
self-report, people naming themselves from a limited list of options. With new
genomic technologies and the informationalization of race, could the future hold the
possibility of racial identity being determined by a single location on the genome, or
a common haplotype? Even through self-report, what people imagine themselves to
be is intimately bound up with who they are told they are by social norms. One’s
racial identity is given at birth. Once genotype creeps into the popular imagination
alongside phenotype, like blood, the genomic structure of race re-locates that
imagination under the skin. These various changes show how the original racial
categories, proposed by Linneaus and then Blumenbach, have been particularly
malleable and subject to political negotiation. The addition of the ‘Ethnicity’
categories in 1977 was the latest efforts of lobbyists and advocacy groups.
A number of criticisms of the OMB categories state that the US population
has become increasingly diverse (American Anthropological Association 1997; Ver
Ploeg 2004). This position indicates that the population was moderately diverse
before the institution of the four racial and two ethnic categories in 1977. It is
indicative of a historical amnesia of the history of the United States. The inaccuracy

projects of U.S. immigration history. Entrance into whiteness was intimately bound
to changing notions of citizenship, naturalization laws, and white ethnic
consolidation. See also Michael Rogin (1996) Blackface, White Noise: Jewish
Immigrants in the Hollywood Melting Pot and Karen Brodkin (1998) How Jews
Became White Folks and What That Says About Race in America on how Jews
navigated between racial other and white through constructing their own identities in
different eras.

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of this statement may not be in the quantifiable position of being diverse or not
diverse, but in the changing nature and political boundaries of the concept of
diversity. Obviously, the US has historically been diverse. Whether this is measured
in terms of the different European ethnic groups, Native peoples, or African
populations that have existed since the dawn of America, or the subsequent groups
that have characterized the formation of US citizenry, this country has never been
homogenous. However, diversity as part of the imagination of American identity has
transformed in the late twentieth century and diversity has become part of the
imagination of American identity, a cultural ‘goal,’ and political issue. The 1977
OMB categories are reflective of the major state recognized racial groups. As
mentioned above, they also reflect the changing politics of identity with the inclusion
of two ethnic categories aimed at people of Hispanic decent. The categories are not
reflective, however, of the lived experiences of people’s racial and ethnic identity or
the extent of different identities. Identity is treated in a limited and homogenous
fashion. They are limited in the extent of distinct cultural and national origin groups
as well as the collapsing of these groups into static categories. In genome research
that is concerned with the variation between diverse groups, in terms of both socio-
cultural and genetic markers, some scientists and bioethicists view the OMB
categories as a barrier to understanding diversity as the categories force individuals
into proxy groups (Interview 1022).
Mounting criticisms of the categories and their inadequacy in accounting for
diversity lead to a governmental review that began in 1993, the same year as the
enactment of the NIH Revitalization Act (Nobles 2004). After four years of

129
consultation and debate that included Congressional hearings, public hearings in
Boston, Denver, San Francisco, and Honolulu, a conference organized by the
National Academy of Sciences, a review by an OMB instituted Interagency
Committee, and research (both secondary and primary) by a subcommittee, the OMD
declined to overhaul the categories. Instead, the OMB did allow respondents to
report multiple categories by checking more than one box. The designations
remained, however, like Linneaus to Blumenbach, a racial category was added, from
four to five, and an addition of “Latino” to the ethnic categories: for race, American

Indian or Alaska Native, Asian, black or African American, Native

Hawaiian
8
or
Other Pacific Islander, and white, and for ethnicity, Hispanic or Latino and not
Hispanic or Latino. The OMB maintained the position that these were “basic”
categories. While they were not “biological or genetic,” they were to be thought of in
terms of culture and ancestry (US Office of Management and Budget 1995).
However, the OMB provided no distinction between race and ethnicity.
In a “Special Communication” in the Journal of the American Medical
Association, Kaplan (2003) outlines a number of common criticisms of the OMB
categories and the October 1997 revisions. The OMB Directive 15 of 1977 added
ethnicity to the US census. According to the state organization, there are only two
ethnicities, Hispanic or not Hispanic, and now, Hispanic or Latino and not Hispanic
or Latino. As mentioned above, respondents were allowed to check more than one
category to indicate a multiple racial identity but the OMD would not include a

8
The inclusion of the Native Hawaiian category was largely the result of the OMB
receiving 7000 postcards from Hawaiians (Marshall 1998).

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multiracial category (Skerry 2000). The issue of multiracial identity was contentious
as civil rights groups opposed its inclusion while other groups marched on
Washington in support of the change (Bowker and Star 1999). The changing status
of the categories indicates their political nature and the compromises made in the
creation of standards. Racial classification has been discussed as a mode of state
surveillance (Bowker and Star 1999). Racial categories and their maintenance have
also used to organize political voice for minority populations. The latest wave of
civil rights has turned to the sphere of health in the 1990s. Minority groups often
marshal state statistics to illustrate health and health care disparities between racial
groups. Critical theorists have referred to this type strategy as strategic essentialism.
Kaplan also suggests, however, that the classification system homogenizes disparate
groups and erases within group diversity. Becerra et al’s (1991) seminal study shows
how birth weights and infant mortality rates differ between Puerto Rican and Cuban
ethnic groups that would be included under “Hispanic or Latino” (See also Marshall
1998). Ver Ploeg (2004) adds that public health agencies need more refined
information on local communities to target programs and interventions, such as
Filipinos and Japanese. The American Anthropological Association (1997) published
a response to Directive 15 shortly after the release of the revisions. Among a number
of recommendations, the authors suggested that the category of race be eliminated
altogether by the 2010 census due to its racist nature and as an unscientific category
(See also Lawrence 1997). Presently, there exists the problem of seemingly
contradictory deployments of race: how do we pay attention to the systemic
exclusion of minority populations from access to health and the subsequent

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disparities in health without reproducing the conditions that perpetuated inequality?
How do we combat racism, without re-producing the racial order? The NIH
attempted to answer this question in a 1993 initiative administered by the Clinton
administration.

The NIH Revitalization Act of 1993
While the OMB was beginning the process of revising Directive 15, Congress was
putting the finishing touches on new legislation that would institutionalize diversity
in biomedical and scientific research. After decades of women, sexual minorities,
and people of color protesting against the traditional biomedical model and
becoming part of medical and scientific institutions, President Clinton signed the
NIH Revitalization Act of 1993. The 1993 act of Congress ensured that women and
minorities must be included as subjects in clinical research in order to receive public
funding from 1995 onwards. In 1994, the NIH released “NIH Guidelines on the
Inclusion of Women and Minorities as Subjects in Clinical Research,” which states,
…the guidelines published here are intended to ensure that all future NIH-
supported biomedical and behavioral research involving human subjects will
be carried out in a manner sufficient to elicit information about individuals of
both genders and the diverse racial and ethnic groups and, in the case of
clinical trials, to examine differential effects on such groups. Increased
attention, therefore, must be given to gender, race, and ethnicity in earlier
stages of research to allow for informed decisions at the Phase III clinical
trial stage. (NIH 1994)

The act also mandated and office of Research on Women’s Health and an Office of
Research on Minority Health. In addition to the inclusion of women and minorities
and the focus on variation and difference, the cost of a study was not acceptable as

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grounds for exemption. The Revitalization Act did not, however, state how to
operationalize racial minorities. The NIH followed the direction of other government
agencies by adopting the categories from the census set by the OMB in 1977. The
NIH uses the five race and two ethnicity categories for evaluating grant applications
and assessing compliance with the act. When researchers apply for grants, they must
indicate how their protocol includes women and minorities and offer rationales if the
research design is not inclusive. The OMB coding scheme also allows comparison
with other databases, especially ones concerned with health (Epstein 2003). In the
same year, the FDA released a similar set of guidelines for conducting drug trials.
While the increased attention to difference in a progressive fashion was new in the
1990s, accounting for difference was not. Epstein suggests that the “real questions, at
each historical moment, have less to do with whether differences are recognized than
with precisely how they are imagined and taken into account and where differences
are understood to be located” (191). By 2000, the NIH issued guidelines with
alterations in the language to include “sex/gender” instead of “women” and “and/or”
race/ethnicity (NIH 2000). In terms of gender, the NIH changed from a position of
advocacy for the inclusion of women to a position of gender neutrality.
The changes in NIH policy were not received without controversy. Some
supported the inclusion of race as a variable in research while others called for the
end of its use. Many groups applauded the policy changes as a break from a history
of discrimination and exclusion of minorities in science and medicine. For example,
African American men who had syphilis were treated with placebos without their
knowledge in the Tuskegee experiments (Jones 1981). Others who supported these

133
measures argued that “they heralded the end of privileging the health research needs
of white men” (Reardon 2005: 153). However, color conscious medical research also
ushered in a new phase that re-articulated disease and racial difference. During the
1990s, increasing evidence emerged that suggested there exists an ethnic
differentiation of diseases, such as high rates of sickle cell among African Americans
and Tay Sachs among Ashkenazi Jews (Duster 2003), and ethnic differences in drug
response (Tate and Goldstein 2004). Historically, different racial groups have been
stigmatized as ill and diseased as a strategy of colonial control and surveillance
(Gilman 1985, 1988; Levine 2003). Critics of the NIH guidelines charges that
focusing on race in clinical research would have detrimental effects for racialized
groups, if not racist outcomes. Brawley (1995), a noted African American
oncologist, argues that the guidelines continue to link race and biology spuriously.
The implementation of the Guidelines may eventually do more harm than
good for the minority populations that it hopes to benefit. The legislation’s
emphasis on potential racial differences fosters the racism that its creators
want to abrogate by establishing government-sponsored research on the basis
of the belief that there are significant biological differences among the races.
(293)

Focusing on the links between race and disease not only reinforces the biologizing of
race, but it attributes health outcomes to genetics rather than environmental factors
such as economics and diet. Some went even further to insist that race be abandoned,
as it has no biological basis (Freeman 1998). While there may be no right answer to
the dilemmas that arose out of the Revitalization Act, the discussions are more
illustrative of the changing racial politics. The line between racist and non-racist
scientific research and medical practice became increasingly blurred in the 1990s.

134
While these debates gained momentum and attention, disrupting long held traditions
in scientific research and medical practice along the way, race thinking and race
became further embedded in the discourse and research of the biomedical fields.
Science and medical journals responded to institutional and social changes by self-
regulation through editorial policies in the use of race and ethnicity as variables in
journal submissions.

Editorial Policies on Race and Ethnicity (EPRE), 1991-2004
Discussions about the use and effects of race and ethnicity in biomedical and
scientific research first appeared in the area of public health in the United Kingdom.
Two papers published in the Journal of Public Health in 1991 and 1992 prompted
the early journal debates. Bhopal (1991) argued that describing people as ‘Asian’ in
the context of the UK was inappropriate. ‘Asian’ simultaneously homogenizes
everyone from the Indian subcontinent and excludes those in Britain from south and
east Asia. The concept is also lacks scientific clarity and signifies an Other racial
identity, i.e. not white and, therefore, an outsider in British culture. This seminal
paper was a call for a debate that crosses all sectors of biomedical and scientific
research with the aim of establishing international principles for the classification
and description of ethnic groups. In the following year, Sheldon and Parker (1992)
pointed out that race and ethnicity were increasingly being used in health research,
however, the data was being collected in an ad hoc manner. He argued that the
concepts of race and ethnicity were being employed uncritically and without
discussion of the nature of the constructs, which was leading to inconsistency in their

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use. The danger in race being treated as a statistical variable, according to Sheldon
and Parker, was that stereotypes could be reinforced. Also, the emerging research
tended to focus on disease rather than environmental factors that could attribute to
health outcomes. Shortly after these two papers, the debate migrated into the larger
sphere of medicine in the British Medical Journal (BMJ).
In a 1994 issue of BMJ, Senior and Bhopal and McKenzie and Crowcroft
each tried to de-couple race and ethnicity. Senior and Bhopal differentiate race and
ethnicity by locating the history of race in taxonomy of the eighteenth century and
epidemiology as “physical characteristics,” “classification.,” “evolution,”
“variation,” and geography (327-8). They claim that there is increasing agreement
that race is a “social and political phenomenon” and “more useful for social rather
than biological explanations

of variations in the prevalence of disease” (Ibid).
Ethnicity is also a “socially constructed phenomenon” whose “boundaries are
imprecise and fluid” (Ibid). The two terms tend to be used interchangeable with the
underlying assumption that differences in disease frequencies between groups can be
genetic. Often ethnicity acts as a euphemism for race and is used to avoid
associations with racism. Skin color (observed rather than self reported), country of
birth, names, and self-classification, what would come to be known as “self-
identify,” are all identified as means of indexing ethnicity. Especially in the use of
skin color, ethnicity tends to become a proxy for race. In spite of the shortcomings of
the use of ethnicity, the authors make a number of recommendations to improve its
value in research such as researchers stating explicitly how they classified ethnic
groups, reflexivity in regard to ethnocentrism in personal values, recognizing the

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contingency of producing data on dynamic categories, and including the relevance of
environmental factors in variations of disease.
McKenzie and Crowcroft (1994) elaborate on Senior and Bhopal by finding
that the problems with race and ethnicity are in the lack of clear definitions,
inconsistency in use, and arbitrary group assignment. Another problematic concept,
culture, is also used with varying definitions and applications. The authors explain
that the biological basis of race is undermined by findings in genetics that show there
is more variation within ‘racial’ groups than between them. Ethnicity is equally
problematic. For example, they cite a longitudinal study where people self-reported
different ethnicities in two different years. Even widely used categories such as
Asian, white, and black are heterogeneous and defy easy categorization. They also
tend to obfuscate local variations in the conditions of health services and disparities
in health. “To discover why different groups have different

experiences of health and
what can be done to redress the balance

we need to disentangle the influences of
racism, education,

unemployment, and social deprivation” (287). Calling for an
“investigation of the validity of the current classifications” that employs the same
rigorous approach as used in the field of biomedical research, they nod to Senior and
Bhopal’s recommendations. Interestingly, these early papers discussed culture
alongside race and ethnicity, treating it as another variable that stands for group
identity. This shows how culture would often times stand in as a more acceptable yet
coded way of expressing racial categories. The work by Bhopal, Senior, McKenzie,
and Crowcroft were precursors to the first editorial policy on race and ethnicity.

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The British Medical Journal published guidelines for the reporting and use of
race, ethnicity, and culture in the April issue of 1996 for describing individuals and
groups. The guidelines focus on the differences between the concepts and their
contingent, imprecise, but important role in biomedical research. The BMJ editor
urges authors to describe the logic behind their choice of categories and be as
descriptive as possible about the identity of the participant, instead of using “catch
all terms in common use” (1094). The process of classification should closely match
the hypothesis as well. The guidelines also make a distinction between biological
research and health services research, stating that ethnicity and culture are useful for
the latter, but limited for the former. The BMJ guidelines and the preceding
discussions tend to construct a continuum of fluidity between race, ethnicity, and
culture. On one side of the continuum, race is treated as the most static and
biologically determined concept, even though many of the authors state that it is
socially constructed. Occupying the opposite limit is culture, whose lines of
demarcation, states the guidelines, are always arbitrary. Ethnicity sits between race
and culture. In spite of its explanatory limits, there is an attempt to recoup ethnicity.
While the debates about bio-race and genetics in public health, biomedicine,
and science resurfaced in the late 1980s and early 1990s, social scientists and
cultural critics had been deconstructing race as a biological concept and
reconfiguring ethnicity. Stuart Hall (1988), Robert Miles (1989), and others are also
arguing for the abandonment of race as a marker of social identity. Race reduces
difference to hierarchical and static notions of culture and group belonging. It has no
validity as a biological concept and there is no scientific justification for race

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identifying a set number of human groups arranged in a hierarchy. Miles states that
race is an idea that should be “explicitly and consistently confined to the dustbin of
analytically useless terms” (1989:72). Racial identity is indeed a dynamic and fluid
process intimately bound to the social order and power relations. While race is a
social and political concept, it may be too bound up in histories of colonialization,
institutional racism, and genetic determinism. Instead, group identity needs to
account for changing forces of migration, language, culture, and nationality.
Ethnicity, Hall argues, “acknowledges the place of history, language and culture in
the construction of subjectivity and identity, as well as the fact that all discourse is
placed, positioned, situated, and all knowledge is contextual” (1996:446). Like Hall,
the BMJ seeks to engage rather than suppress difference and decouple ethnicity from
its equivalence with race and racism. This is symptomatic of a turn in the 1990s
where cultural politics at once recognized difference while attempting to subsume
difference in a discourse of sameness.
In an editorial in the same issue of the BMJ guidelines, McKenzie and
Crowcroft, two of the leading advocates in biomedicine who assisted in devising the
guidelines, continue to recommend that ethnicity be considered in an in depth
manner in the context of a range of environmental factors. The categories set out by
the Office of Population

Censuses and Surveys (OPCS) in the 1991 census should be
starting points, they argue, not homogenizing, catch-all categories for a number of
other types of information, including diet and socio-economic status. In 1991, the
official categories were White and Ethnic Minorities comprising of Black, South
Asian, Chinese, and Others (NEMDA 1991). The minority categories were further

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defined as Black-Caribbean, Black-African, Black-Other, Indian, Pakistani,
Bangladeshi, Other-Asian, and Other-Other.
9
The final category, “Other-Other”
would not seem to have any relevance to race, ethnicity, or culture and, obviously,
would require the additional information the authors call for. Discursively, Other-
Other erases any sorts of history, health or material, political organization, or basic
recognition within the nation state. The “Other-Other” individuals are counted but
rendered categorically invisible. Because of the shortcomings of the OPCS
categories, McKenzie and Crowcroft maintain the need for detailed demographic and
environmental local data measured against categories that can be compared across
geography, time, and census data. Calling for the abandonment of racial and ethnic
data in favor of colorblind practices would make invisible the social disparities in
health outcomes and health services. At the same time, however, reproducing racial
and ethnic categories has the tendency to reify group differences and locate them in
biology.
Journals across the Atlantic in the US did not pick up on this trend of
editorial policies on race and ethnicity (EPREs) until the turn of the century. This,
despite Osbourne and Feit’s challenge, in 1992, to the editors of the Journal of
American Medical Association to “do no harm” in the journal’s articles that study on
racial difference. They argue that using race as a variable in research reduces
medical causes to genetics, rather than environmental factors. Also, the authors state

9
The OPCS was subsumed under the Office for National Statistics in 1996, which
revised the categories for the 2001 census. In the major group, Mixed, Asian British,
and Black British were added. Also, South Asian was changed to “Asian or Asian
Other” and Chinese and Other became separate major categories (Office for National
Statistics 2001). Can be accessed at www.statistics-gov.uk

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that comparisons of medical conditions between racial groups leads readers to
“assume that certain racial groups have a special predisposition to, risk, or
susceptibility to the illness studied” (275). Calling this a “subtle form of racism” and
implicating the practice of biomedical research, they appeal to the Hippocratic oath
in urging researchers to write about race in an ethical manner. It was more than ten
years before JAMA would address this issue and not until the late 1990s that
American journals began to follow the lead of their British colleagues.
Like the UK, the first area to publish an article addressing the issue of race
and ethnicity in biomedical research was in public health. In 1998, Raj Bhopal, the
British doctor and professor of public health, sought a wider audience in the
American Journal of Public Health. Along with Donaldson, Bhopal sought to widen
the race and ethnicity debate by questioning the use of the term ‘white.’ Bhopal
points out that ‘white’ is often used but rarely defined even though it is often
compared to many non-white groups. Similar to Osborne and Feit (1992) and
Sheldon and Parker (1992), he argues that such lack of clarity and inconsistency
leaves the reader to think about such groups in stereotypical terms. Often in this type
of research, race is presented as an independent variable rather than a social and
political process that interacts with its material and ideological surroundings.
Further, race is also assumed to be a mutually exclusive category, which
homogenizes heterogeneous populations, whether they are black or white, for
example. As discussed above, biomedical and scientific research often relies on the
racial and ethnic categories set out by the state, which themselves are not absolute,
mutually exclusive, or static formations. In the case of the US, the Office of

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Management and Budget sets the standards and, in the UK, it is the categories used
by Office of Population Censuses and Surveys. In the same edition, Fullilove
suggested that the term ‘race’ be abandoned in public health research (1998).
Oppenheimer (2001) called this solution a case of “paradigm lost” (2001). He
wonders if the term race can be easily expunged and if there is something to be lost
analytically in the process. Also, Oppenheimer questions the neutrality of ethnicity,
which has its own set of negative associations.
By 2000, the conflict of interest policies in biomedical and public health
journals discussed above had become the norm, but EPREs were only beginning to
become institutionalized and, unsurprisingly, gathered much animated attention. In
fact, what can be called the great race debate had begun to ramp up in the biomedical
and scientific journals since the mid 1990s. In epidemiology, public health,
oncology, genetics, medicine, pediatrics, dentistry, pharmacogenomics, to name a
few domains, race and ethnicity and their pitfalls, inconsistencies, misuses, and
opportunities had become part of the general journal discourse. The adoption of
EPREs is a case where the content of biomedical and science research met the social
and political changes of the time and where activism moved from discussion to
practice to institutionalization, however unevenly. The widespread adoption of
biomedical journals was largely due to the International Committee of Journal
Editors.
Three years after the Asilomar conference a group of editors of medical
journals met in an unremarkable meeting in Canada to discuss the future of their
field. While the agenda for the 1975 meeting was heavy on ethical ramifications of

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recombinant DNA research and government intervention, the heady debates that
characterized Asilomar were probably not replicated in the shadows of the north
shore mountains of Vancouver. The Vancouver Group, as they would come to be
known, gathered to discuss common formatting procedures for manuscripts, such as
aligning with National Library of Medicine standards for a bibliography. They
published its first set of requirements in 1979. In subsequent years, however, the
collection of editors expanded in size and evolved in scope to become the
International Committee of Medical Journal Editors (ICMJE). As social movements
prompted transformations in the biomedical sciences over the past three decades, the
ICMJE continued to address ethical issues. By 1997, their mandate had expanded in
scope beyond table formatting and pagination to patient privacy and ethics (ICMJE
1997). In 2000, language was added that directed authors to explicitly justify the
inclusion and relevance of race and ethnicity as variables (ICMJE 2006).
Hundreds of biomedical journals follow the ICMJE requirements including
the New England Journal of Medicine (NEJM) and the Journal of the America
Medical Association. Even though NEJM and JAMA subscribed to the ICMJE
guidelines, neither has instituted EPREs in 2000. However, in 2001 Richard
Schwartz, an editor of NEJM, condemned the practice of “racial profiling” in
medicine and echoed the argument that race is not a scientific concept.
As for medical research, any investigation that entails so-called racial
distinctions, whether a clinical trial or a laboratory study, should begin with a
plausible, clearly defined, and testable hypothesis. Before studying a possible
relation between skin color and sodium excretion, for instance, investigators
should have a credible reason for believing that such a link could exist and a
plan for finding the relevant genetic network. Research to root out social

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injustice in medical practice needs continued support, but tax supported
trolling of data bases to find racial distinctions in human biology must end.
(2001:1393)

Schwartz goes further than Osborne and Feit, who wrote almost ten years earlier, and
called for the end of doctors using race as a proxy for genetic variation and disease.
He also indicates the emerging proliferation of database and practices of knowledge
production in genetics and refers to race-based medical research as “pseudoscience”
(2001:1392). As Phimister, another NEJM editor, points out though science is in an
age of discovery. She argues that it would be unwise to abandon race “when we have
barely begun to understand the architecture of the human genome” (2003:1081).
These two commentaries by NEJM editors show the disagreements in the biomedical
sciences about the place and role of race in research and clinical practice. For
example, Cooper et al (2003) argue that the history of racism and potential for future
racial discrimination is cause for excluding race from biomedical and genetic studies.
In the same issue of NEJM, Burchard et al (2003) contend that the only way to
uncover inequality in health practices and policies is by recording race. While the
New England Journal of Medicine was a leader in the issue of conflict of interest and
journal publication, it has lagged behind in establishing EPREs. As of the writing of
this dissertation in 2006, the journal does not have a stated EPRE, although it is
listed on the ICMJE website. In 2003, Kaplan set out a number of recommendations
in JAMA. The journal expanded on the ICMJE guidelines and instituted their own
policy the next year (Winker 2004). Reflecting the BMJ’s policies, authors were
required to fully explain the rational and significance of using race or ethnicity as a
variable and attempt to measure as many other environmental variables as possible.

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The guidelines and policies on race and ethnicity by the ICMJE, JAMA, and NEJM
show how ubiquitous the issue had become. They also signal a turn from appeals for
abandoning race to instructions on how to rationalize its use. If one could identify the
impetus for this change in direction at the turn of the millennium, it would have to be
the intersection of disease research and genetics, the Human Genome Project.
Nature was the first scientific journal to institute an EPRE. Nature Genetics
(NG), a node in the network of Nature journals, requires authors to “explain why
they make use of a particular ethnic population and how classification was achieved,
and are asking reviewers to consider those parameters when judging the merits of a
manuscript” (Nature Genetics 2000). The editorial discusses the limits of the OMB
categories and the constraints put on researchers by the NIH adoption of such
categories in the Revitalization Act of 1993 as well as the ethnic differentiation of
disease and genetic variation. Medical and public health guidelines focused on
environmental factors and a breadth of epidemiological data. NG mentions this issue
in passing, but focuses mainly on the problem of definition, terminology, and
precision. One of the main barriers the editorial identifies is the combination of the
OMB categories and “sloppy language” that comes from “poorly defined” lay
conceptions of race and ethnicity. A bioethicist from the HapMap project succinctly
called this shift, from race to a combination of factors, from proxy to precision.
…we don’t really want to change the paradigm from race to human genome
variation. It may be human genome variation. But it also may be diet. It may
be socio-economic status. It may be something else in terms of what specific
variable we are looking at. So, now I am talking about changing from proxy
to precision. And the precision is based on what it is we are trying to find.
(Interview 1022)

145
Instead of using race to stand in for a range of possible environmental or genetic
factors, studies must be more specific in what their research questions are trying to
identify. This bioethicist agreed that one of the main barriers to precision and
reproducing imprecise groups, at best, and the biologizing of race is the OMB
categories. She explains, “It forces you to put people in these categories” (Ibid). The
paradox in scientists using race or ethnicity as a variable lies in the attempt at
precision using an inherently imprecise concept and blunt instrument.
The adoption of an EPRE by Nature Genetics is significant as it is a leading
journal in the scientific community. Journal adoption of EPREs in the fields of
science, medicine, pharmacogenomics, and biotechnology has been uneven and
slow, and not without controversy. In the Pharmacogenomics Journal, Nebert
(2001), writes that the Nature Genetics policy for requiring authors to explain their
process of classification is an important step in doing better science and better
decision making in care and prevention. However, the author also adds, “This should
be accomplished by mechanisms based on scientific reason, rather than mandates for
‘racial inclusion’ in all human studies.” A notable absence is the leading scientific
journal of record, Science. Many HapMap interview respondents expressed
reservations about the enforcement of such policies. While they welcomed this
mechanism to address the problems with using race and ethnicity as categories of
scientific research, there was common concern that either the policies were not being
adopted widely enough, or, when they were institutionalized, the editors and
reviewers were not making enough effort to ensure that articles accepted for
publication had stated rationales for using race or ethnicity. One respondent

146
suggested that there is less need to justify hypotheses with superficial assumptions
currently, where authors had to much more in the 1990s. At best, the impact of
editorial policies of transparency has been neither strong nor effective (Interview
1022).

Conclusion
There are a number of regulatory components that institutionally enable and
constrain the HapMap Project. This chapter has described three major regulatory
areas that structure the HapMap Project institutionally: the deregulation of
university-industrial relations, journal conflict of interest policies, and government
and journal policies on race and ethnicity. Academic biologists took their first steps
out of the labs towards private industry in the 1970s. Chemistry and physics had long
been engaged in collaborations with commercial entities, however private interest in
biology was quite new. The discovery of rDNA and the Chakrabarty case signaled
the emergence of genetic engineering and biotechnology and the recognition by
government of the economic and scientific potential of this new sector. In an age of
neoliberal economic policies there exists an ideology that the market will operate in a
natural and productive fashion without the interference of the state. As the first part
of this chapter showed, collaborations between the academy and industry were
facilitated and encouraged by government deregulation. Primarily, through state
participation genome science has progressed, notably through funding for projects,
such as the Human Genome Project and HapMap, from the National Instituted of
Health.

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Through the process of deregulation and collaboration with the private sector,
the culture and practices in university labs underwent industrialization. As private
funded research in science, both in industry and academia, increased and many
university labs partnered with firms, professors began donning two hats by holding
academic posts and working for biotech. This prompted many scientists to question
the motivations of pure research. For some, the ‘soul’ of academic research and the
integrity of research goals and findings was at stake. Journals responded by requiring
authors to state whether or not their study was privately funded through conflict of
interest declarations. The concern was that findings from scientific studies would
validate the interests of the entrepreneur, rather than ‘pure’ knowledge or the public
good. Scholars such as Dorothy Nelkin argued, “Science is a big business, a costly
enterprise commonly funded by corporations and driven by the logic of the market.
Entrepreneurial values, economic interests, and the promise of profits are shaping the
scientific ethos” (1998:893).
Finally, due to pressures from the great social movements at the end of the
twentieth century, a new common sense of diversity in research practices became
institutionalized in color consciousness government and journal policies. Racial and
ethnic minority populations who were marginalized and mistreated in biomedical
research and clinical practice became visible participants through the NIH
Revitalization Act of 1993. While the racial and ethnic categories set out by the
OMB in 1977 largely dictated the terms of identity in research and clinical practice,
public health, medicine, and genetic science struggled with histories of
discrimination and imprecise racial and ethnic categories. Discussions about how to

148
properly identify minority communities began in public health journals and migrated
across medicine and genetics, prompting a number of journals to institute editorial
policies. These regulatory changes play an important role in researching different
social groups.
As a population geneticist from the HapMap Project comments, “We could
have gone forward with the sequencing without bringing other populations using the
CEPH samples (DNA samples collected a stored at the Centre d’Etude du
Polymorphisme Humain in the France). But we didn’t want to use only white
Northern Europeans and potentially miss out on the variation that exists among
Asian, Africans and other populations. We are trying to be as inclusive as possible”
(Lee and Koenig 2003:235). While the speaker locates the decision making process
among the members of the project, the institutional and cultural structures are
evident. As discussed above, diversity among research subjects has become more
prevalent in biomedical research. Further, NIH funding guidelines from the
Revitalization Act of 1993 stipulates the inclusion of minorities. HapMap comes out
of the NHGRI, one of the institutes of the NIH. In spite of the Act and the
ubiquitousness of diversity, the statement also reveals how the white European is
normative in scientific research. The readily available samples are European, rather
than from a minority group. Chapter Five will examine cultural discourses about race
and populations that circulate in genomics.

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Chapter 5
Discursive Formations of the Informationalization of Race:
Race Talk in Genomics and the HapMap Project

The informationalization of race encapsulates new mechanisms of racialization in a
post-civil rights, information age where technology and identity become increasingly
intertwined. The third component of this analysis shifts to the realm of culture.
Scientific discourse about the genome and race is embedded in cultural assumptions
and power relations about the nature of human identity and behavior and the ‘proper’
social order. When I first began conceptualizing the relationship between science and
race, my hypothesis placed race as the dependent variable and science the
independent variable. I began to problematize this popular notion of the relationship
between science and culture scientific research affects the larger society, operating
from a position of neutrality and objective truth. This chapter takes the position that
the relationship between science and race is a recursive relationship. At times,
however, there is strong evidence that science is the dependent variable and common
sense racial knowledge informs the way in which scientists design their own
research, ask questions, and report results. Through a textual analysis of leading
journals in the biomedical sciences, statements on science and race, and interviews
with members of the HapMap Project, this chapter explores the central discursive
frames of the informationalization of race and how they are manifested in race talk in
genomics. Fifty years after the UNESCO Statements on race and decades of the

150
social sciences, the humanities, and the natural sciences battling to wrench race away
from its biological moorings, genomics has re-ignited the debate on race.
Race talk in genomics and the biomedical sciences has emerged within a
discursive and social context that is much different from the context of the UNESCO
statements half a century ago. Bonilla-Silva (2001, 2003), Brown et al (2003), and
Kim (1999) and others argue the current dominant racial ideology is colorblind
racism. Bell (1995) and Wolfe (1998), however, suggest that we are in a time of
racial realism. Both may be right. One of the main interventions of the UNESCO
authors was to argue that race is a social construct rather than a biological one. This
position has been repeated often by scholars of race since then and has, largely,
become common sense. The distinction between the existence of races and race as a
social and political identity has largely defined the differences between
racist/conservative and anti-racist/progressive discourse (See Figure 1, Appendix A).
In the post-Civil Rights era, however, what counted as good liberalism 30 years ago
is now seen as centrist and in contrast to progressive politics of anti-racist groups,
who often organize around race or some other essential identity category, such as
gender or sexuality. Further, discourse has gone “underground” where race talk is
coded and positions that once were the domain of liberalism have now been co-opted
by conservative agendas. The social and political context of racial politics has shifted
dramatically since the 1950s and, especially, in the last three decades. In spite of
these challenges and shifting terrain, I argue there are four central discursive frames

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that characterize the informationalization of race. The first section of this chapter
delineates the four frames and then I will discuss each of them in the context of race
talk in biomedical research and genomics.
Figure 2 (Appendix A) describes the four positions in the field of race talk in
cultural discourse. Along the vertical axis lies “race/no race.” These two positions
account for discursive positions that argue for or against the existence of race either
scientifically or socio-politically. Along the horizontal axis lies the positions
“progressive/conservative.” Progressive, anti-racist politics traditionally have argued
against race through the deconstruction of race as a biological and hierarchical
category and political mobilization against structural discrimination. Conservatives
upheld a system of racial domination under Jim Crow, based on an ideology of
‘inherent’ inferiority of ‘races.’ In the 1990s, conservatism has adopted the discourse
of multiculturalism and diversity, but not structural equality. More recently, neo-
conservatism has returned to overt racial rhetoric, albeit in a ‘softer’ form. Also, the
axes do not act as impermeable boundaries between the different frames. Signifiers
can slide towards different positions or be shared by two or more of them. For
example, both planetary humanism and colorblindness advocate for the abolition of
racial categories. However, the former seeks social equality and the latter seeks to
uphold the status quo.
Race talk in the informationalization of race takes shape in a field of four
positions or discursive formations: strategic essentialism, planetary humanism, racial
realism, and colorblindness. Gayatri Spivak (1987) has been credited with coining
the term strategic essentialism and it has developed into an important concept in

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postcolonial studies, feminism, queer theory, and critical race. While the strategic
essentialism position may be anti-essentialist, there are instances when action calls
for a strategic use of a fundamental racial category in order to further a group’s
political aims. Hall (1996) and Gilroy (1993) have both argued that a contingent
racial essentialism can be tactically advantageous when minority communities are
bounded by a history of systemic racism. Omi and Winant (1994) refer to this
approach as purposeful racialization and have documented a number of its
instantiations. Planetary humanism, on the other hand, advocates for the complete
abandonment or end of race. Gilroy (2000) argues that new genomic technologies are
able to bypass the epidermal layer that has anchored racial categories and reveal the
truth of who we are at the molecular level. This knowledge can finally lead to the
disruption of race as a regime of truth and move towards a politics of difference
beyond the yoke of bio-race while retaining cultural difference and diversity as
political ideals.
Colorblindness also seeks the end of racial identity, but not the end of racism.
Unlike the futurism of planetary humanism, colorblindness argues that we are at the
end of racism and the persistence of racial inequality is only because minorities have
failed to take advantage of opportunities created by the civil rights revolution. There
is little need for color conscious policies, such as affirmative action, as this position
believes that the US is now a colorblind society. In the 2002 gubernatorial elections
in California, UC Regent Ward Connelly, best know as the architect of Prop 209 that

153
struck down affirmative action in college admissions, advocated Prop 54 which
banned government agencies or government funded organizations from collecting
statistics on race.
Finally, the racial realist position also believes that human groups are
fundamentally divided along racial lines. The concept of racial realism comes from
the work of Derrick Bell (1990, 1992) who argued at the end of the 1980s that gains
from the civil rights movement were being lost in the fervor of the rising reformist
movement. Neo-conservative racial realists, on the other hand, argue that race is
biological and that race reveals basic group differences. For example, Herrnstein and
Murray (1994) suggest that there are differences between races in IQ while Rushton
(2005) argues that different races have varying levels of criminality and sexual
aggressiveness. In the remainder of this chapter, I explore how the cultural frames
that characterize the informationalization of race structure the discourse new genetic
research into the human genome.
In The Century of the Gene, feminist scientist Evelyn Fox Keller argues that
gene talk has become outdated in terms of knowledge about the function of genes.
The entrenchment of the gene in science and the popular imagination, however, is
not only a function of scientific discovery but scientific discourse. Keller argues that
science is, in part, discursively constructed and that words enable and constrain what
scientists can say, think, and hypothesize. Scientific facts and the creation of
knowledge are not independent of discourse. Nor is discourse merely reflective of
the social and scientific world, but constitutive of it.

154
Like the rest of us, scientists are language-speaking actors. The words they
use play a crucial (and, more often than not, indispensable) role in motivating
them to act, in directing their attention, in framing their questions, and in
guiding their experimental efforts. By their words, their very landscapes of
possibility are shaped…What is missing – and would be absolutely required
for understanding the role of language in biological research – is a far deeper
investigation of the material, economic, and social context in which that
language functions.” (Keller 2000:138-9)

Keller draws on the work of social theorists Saussure, Barthes, and Derrida to show
how language is intimately connected to social action. Donna Haraway has shown
how gene mapping and technoscience involves the inhabiting of stories (Haraway
1997). In this regard, genes have a social life, that is, stories about them are
circulated through scientific circles as well as media representations and everyday
discourse. Moving from the micro level of the individual scientist to macro level of
social structure, Foucault has shown how modern power operates through a web of
discourses. The formation of modern biology is the result of a 200 year-old
“complex web of semiotic-material practices” focused on the body (Haraway
1997:217). Identity formation is not based on a unitary identity but emanates through
a number of strategic points of negotiation over the meaning of identification and
difference. Discursive formations are established hierarchical orders of ‘truth’ that
organize language and determine communicative practices in a particular historical
moment (Foucault 1972; Hall 1997). Put another way, the manner in which we talk
and think about subjects or issues is limited to the choices of frames available to an
actor in a given historical and social context. Discursive formations structure ways of
thinking and storytelling that reinforce already taken for granted notions of identity
and difference while limiting alternative constitutions (Lidchi 1997:191). Discourse

155
does not merely reflect social practices, relations, and structures. It plays a
constitutive role in the construction and reproduction of them (Emirbayer and
Goodwin 1994; Wetherell and Potter 1992). The way that scientists conceptualize
and report findings about the race, human difference, and the genome is not simply a
reflection of reality. The language and codes scientists deploy frames not only their
perspectives, but their research design.
Research, editorials, and commentaries network ideas and frameworks of
understanding or semantic networks. Semantic networks are shared cultural
meanings about a particular phenomena; they are meanings by association (Du Gay
1997:15). They connect the descriptive or literal meanings of a word or concept with
broader connotations and cultural discourses or discursive formations. The articles in
the journals, websites, and research projects have their own networks of meaning
about human variation, populations, genomics, and biology. However, they are
connected to broader cultural discourses about genetics, race, and science. From a
micro point of view, they are part of a particular discussion and, at the macro level,
each contribute to a changing discursive formation about race and genomics. The
overall narratives that structure genomic stories are evolving in the sense that each
research paper, editorial, special issues, and commentaries constantly push discursive
possibilities and actual boundaries. The readers are also writers. No entry in the
discussion is a self-contained unit. This dynamic is similar to how Foucault viewed
the borders of books as ambiguously demarcated.

156
…beyond the title, the first lines, and the last full stop, beyond its eternal
configuration and its autonomous form, it is caught up in a system of
references to other books, other texts, other sentences: it is a node within a
network. (1972:23)

Journal articles literally reference other texts in the tradition of academic citation.
However, concepts and language do the cultural work of connecting to previous
frameworks while acting on them in a constantly evolving formation.
Likewise, genomic stories contribute to larger discussions while, at the same time,
being constrained by the topic and the type of storytelling about that topic which is
already underway. They are nodes within a broader semantic network. That is, within
the broader collection of stories across journals, websites, and domains and within
broader cultural formations.
Cultural formations are interrelated symbols that have a “nonmaterial
structure” and are organized in a manner similar to material structures while,
analytically, being separate from them (Alexander and Smith 1993). While human
agency has traditionally been dependent on material (meaning economic and
network) structures, Emirbayer and Goodwin argue “cultural formations are
significant because they both constrain and enable historical actors, in much the
same way as do network structures themselves” (1994:1440). Actors are both
enabled and constrained by cultural formations. They are enabled by “ordering their
understandings of the social world and of themselves, by constructing their identities,
goals, and aspirations, and by rendering certain issues significant or salient and
others not” (1994:1441). In a recursive and relational manner, actors are constrained
by a foreclosing of certain options or possibilities for action as well as having to rely

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on established frames. Throughout his work, Foucault theorizes a model of social
and political power that contrasts traditional top-down configurations. Instead, he
postulates that social power is arranged in a diffuse fashion and works through
strategic points in a web of discourses (1977:26, 1978:95-96, 1980:98). While the
points of power are diffused, this does not necessarily make for a more democratic
formation. Domination can and does exist and persist. Foucault’s conceptualization
of power can be understood as a network theory.
Treating the various journal articles, editorials, commentaries, and websites
as a whole, the themes across the different strings and sub-categories of organization
can be conceptualized as nodes in a network, a semantic network of genomics and
race. The discursive formations or nodes in the semantic network enables and
constrains stories about race, the genome, and science. They are constitutive of the
social and scientific world. As the analysis below will show, the central frames of the
informationalization of race can be found across discursive formations of genomics
and race. Each of the below themes tends to sit in one quadrant of the grid in figure
2. However, true to the complexity of race talk today, this tendency is just that. Strict
classification into mutually exclusive categories can be elusive. First, a brief
discussion about how scientists define race as a variable is necessary.

Definitions of Race in Genomics
Discussions of race and ethnicity tend to start with the dictionary or history.
“Ethnicity is derived from a Greek word meaning a people or tribe” (Senior and
Bhopal 1994), “ethnicity

and culture are ideas derived from social theory” and

158
“characterised by a sense of belonging

or group identity” and “social pressures and
psychological needs; and they are dynamic” (McKenzie and Crowcroft 1994); “Race
is a social construct, not a scientific classification.” (Schwartz 2001:1392); The
Nature Genetics editorial that set the guidelines for the use of race and ethnicity in
submission even began with Jesus:
Jesus Christ was born in Bethlehem because his parents had to register there
with the Roman authorities, who sought information on the population of
their Empire. Roughly two millennia later, the United States government will
ask its citizens to take part in a similar endeavour, the Census 2000… the
concept of race is a social and cultural construction. (Nature Genetics 2000)

Often, definitions of race either are biological, social, or both. While the
mainstream of discussions has given at least some incorporation of the social and
political nature of race, by far the social-political is only mentioned before entering
into a discussion of race as biology or research findings using race as a category of
analysis. Often, biology and culture are conflated which leads to a lack of precision.
While discussions about the proper uses and conceptualizing of race have been
increasing with genomics, race “is frequently employed in a routine an uncritical
manner” (Williams 1994:261). Cooper et al (2003) begin their discussion of race and
genomics by stating the race is a “contentious” topic (1166). It is an “idea that
intrudes on the everyday life of so many people” (Ibid). Race as a concept is modern
and “grew out of the experience of Europeans in naming and organizing populations
encountered in the rapid expansion of their empires” (Ibid). The “plasticity of race”
comes from its “wide range of meanings” that mix “social and biologic ingredients in
varied proportions” (Ibid). Cooper et al’s definition is typical of a blend of social and
biological references in race-talk in the sciences. While the authors state that race

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affects many people, it is relegated to the domain of ideas, rather than political and
cultural practices originating in rational human action or the normal outcome of
social structures. By locating race in a frame of origins, European colonialism, its
formation is in the distant past indicating that it may be remnants we are grappling
with today. There is no mention of its relationship to contemporary structures.
The supporting reference for the Cooper article is Ashley Montagu’s 1964
book The Concept of Race. While Montagu was a central figure in historical debates
within anthropology about race and society and one of the authors of the first
UNESCO statement on race, his book came out in 1964, nearly forty years before the
publication of Cooper et al, which appeared in the New England Journal of
Medicine. This situates social science discussions of race as historical and the
concept of race as a static phenomenon. The status of race in 1964 is profoundly
different than its contemporary origins. Namely, Montagu’s text pre-dates the ‘end’
of the civil rights movement with the 1965 Voting Rights Act. Not only have the
social structures and everyday realities of race changed since the 1960s, social
science scholarship in the very way it approaches race has undergone a revolution as
well. Finally, the authors admit that race is social in nature with the reference to
ideas and the European process of “naming and organizing populations,” however
the “plasticity” of the concept comes from various biological-social concoctions.
Scientific race-talk aims at precision in the biological nature of race, but is limited
and inexact when it comes to the socio-political. The normative mantra in science,
“race is a social construct, not a biological one” (See Gannett 2001, especially S482),
appears to be superficial or, at least, underdeveloped.

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Decades of research in the social sciences and humanities define race in
multifarious ways within the social construction framework. Race as performance
(Spivak 1987), race as politics (Omi and Winant 1994; Seito 1998), race as power
(Bonilla-Silva 2001), race as media representations (Gray 1995), race as segmented
labor force (Hall 1980, Miles 1982), race as nation (Barker 1999; Bhabha 1994, Hall
1997), race as immigration (Jacobson 1998), race as gender (Mohanty 2003), race as
sexuality (Stoler 1995; Young 1995), and race as law (Crenshaw 1995), are just a
few. Biomedical researchers tend to define race quite superficially, as a social
category, at first. Some use a dictionary definition (Nebert et al 2001), suggest its
derivations from the romantic languages (Bamshad et al 2004), or its classification in
anthropology (Cooper et al 2003). However, once the social nature of race has been
acknowledged, the bulk of the discussion usually focuses on the biological nature of
the differences or similarities between groups, such as, which alleles have tendencies
in what group and linkage disequilibrium between populations. According to Duster,
this conflict is widespread in biomedical and scientific research and, sometimes, “in
the brain of a single author” (Rotman 2004:69). Whatever markers are chosen the
method is statistical and normative. Common sense categories are employed rather
than discovering the clusters of people in the data. Even more oddly, some argue that
self-report of racial identity is the best way to determine the categories. When
imprecision is a major issue in determining the utility of research variables (internal
validity), it seems odd that a scientist would leave the definitions and boundaries of
those variables purely up to the subjects.

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Planetary Humanism
One of the main positions in the struggle over racial identity in the twentieth century
has been the validity of the concept. The racial taxonomies of the seventeenth to the
nineteenth centuries and their status as biological entities came under fire in the early
twentieth century from scholars and political activists alike, such as Franz Boas and
W.E.B. du Bois. Social movements in the mid-twentieth century appealed to a
discourse of sameness in the battle for social equality. Scholars largely agree that the
UNESCO statements on race in 1950 and 1952 represent an intellectual and political
paradigm shift in the social and the natural sciences as they renounced race as a
biological phenomenon. On the front lines, African Americans in the United States,
South Asians and Afro-Caribbeans in the UK, and First Nations peoples in Canada
protested from a moral-ethical position that, in essence, ‘we’ are all the same. That
is, people of color have the political and human right to equal treatment and status
under the law and in everyday life. Since color had been such a divisive mechanism
of social organization, then people should be seen not in terms of their skin color or
race but, according to Dr. King, the content of their character. For many groups, the
goal was not only the disruption of racial hierarchies but the destruction of race
itself.
This position has evolved in the twentieth century into what Gilroy (2000)
calls, planetary humanism. The goal of planetary humanism is the abandonment of
bio-race while retaining difference and diversity as political ideals. It differs from
colorblindness in its direct confrontation and call to abandon “race” through an
acknowledgement of

162
The sufferings that raciology has wrought. The most valuable resources for
its elaboration derive from a principled, cross-cultural approach to the history
and literature of extreme situations in which the boundaries of what it means
to be human were being negotiated and tested minute by minute, day by day.
(Gilroy 2000:18)

Gilroy sees genomics as a potentially powerful technology capable of subverting bio-
race gene by gene, allele by allele.
The contemporary focus on the largely hidden potency of genes promotes a
fundamental change in scale in the perception and comprehension of the
human body. This change is not automatic product of only the most recent
scientific developments and needs to be connected to an understanding of
techno-science, particularly biotechnology, over a longer period of time. Its
impact upon the status of old, that is, essentially eighteenth-century, racial
typologies has been inexcusably neglected by most writers on "race. (Gilroy
2000:19)

There are five main frames that planetary humanism operates from in discursive
formations of race in genomics: single origin, 99.9 percent the same, within/between,
the existence of race, and from proxy to precision. Unlike earlier social and scientific
movements, they appeal to both sameness and difference.

Out of Africa: The Single Origin Story
While globalization, migration, and global communication have been producing
hybrid identities or what Hall (1992) refers to as new ethnicities, research into the
human genome reconstructs origin narratives of human history. In the single origin
story, all of humanity began in the eastern part of Africa (Interview 1006). Both the
1950 and 1952 UNESCO statements being by stating that all humans “are derived
from a common stock” (UNESCO 1950, 1952). Closely following this is
displacement theory or the ‘Out of Africa’ story that posits a single group migrated

163
to the old world more than 80,000 years ago (Nebert and Menon 2001; See also
Tishkoff and Kidd 2003). The visual representation of this exodus usually includes
an arrow that vaguely points from somewhere in Africa to the areas now known as
Europe and Asia. The whole of human diversity has been explained by this type of
simple diagram and legitimizes the search in the genome for the ancient origins of
human variation. As mentioned in the previous chapter, Gould writes that
visualization plays an important part in how scientific knowledge is articulated. A
geometric reformulation is key to conceptualization rather than factual information.
Many scientific revolutions have embodied geometric shifts (Gould 1994).
Genomics is such a scientific revolution and there is a significant focus in-group
comparison studies on the ‘founding’ human groups from Asia, Europe, and Africa.
Racial identity is a complex formation that exists at the confluence of
politics, science, culture, and power. While scientists draw on the history of
population genetics, biology, and physical anthropology to rationalize the nature of
the categories they employ as research variables, there is no decoupling the social
meaning of race. Further, the overarching categories are not even derived from
anthropology or population genetics, but the US Office of Management and Budget.
The OMB categories do not represent neutral and natural classifications, but, more
accurately, the history of political and ideological struggle from the top down and the
bottom up over racialization and identity in the United States. The categories
themselves obscure the political wrangling and the history of racial segregation in
the US as they are presented as neutral codes. The OMB categories have always
changed and will continue to do so. Just recently, in the 2000 census allowed people

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to check off more than one racial category. This single revision upends a tradition in
the US of distinct racial groups and a deep seeded fear of mixing, what use to be
referred to as miscegenation and is referred to in genomics as admixture. Interracial
relationships have always been part of the fabric of American society, but the
dividing lines between groups have been carefully policed in the maintenance of a
racial order. The discourse itself in mixing, past and present, suggests some distant
‘founding’ races. Young (1995) has argued that the term “interracial” reproduces
racism as it harks back to nineteenth century ideologies of separate and distinct
races. Admixture, understood in genetics as the “formation of a hybrid population
through the mixing of two ancestral populations” (Jobling and Gill 2004:749), does
the same work of constructing an ideology of ‘pure’ populations.

We Are All 99.9% the Same
The Out of Africa hypothesis was confirmed with the completion of the Human
Genome Project. The historical process of racial classification explicitly stated a
hierarchy of humans ‘races’ in an extension of the Great Chain of Being. In practice,
this often meant that people of color were considered, in varying degrees, to be less
than the white norm. Appeals to the sameness of humanity have been used to combat
an emphasis on difference and dehumanization of the Other. This position received
crucial support with the announcement of the completion of the Human Genome
Project. One of the highly publicized conclusions of the HGP was that that human
genetic makeup is 99.9% the same. President Bill Clinton cited this number in his
speech at the joint announcement of the completion of the draft genome on June 26,

165
2000, and both Francis Collins and Craig Venter have repeated this statement often.
Venter has been a particular advocate of the 99.9% finding and the shared genetic
heritage of humans. In his White House speech, Venter claimed that there is no way
to tell the difference between the five different ethnicities in the Celera samples
(Venter 2000). This statement follows American Anthropological Association’s 1997
“Response to OMB Directive 15,” UNESCO’s Replacement Statement on biological
Aspects of Race issued (1995) revised at a meeting in Italy, and a number of other
scientific associations official positions
10
. Differences in the remaining .1% of the
genome (which is about 3 million nucleotides) have been described as superficial
and meaningless.

The Within/Between Debate
The “99.9%” statement tends to be located near a statement about the differences
between groups: the variation within a population is far greater than the difference
between them. Both original UNESCO statements included similar statements:
“With respect to most, if not all, measurable characters, the difference among
individuals belonging to the same race are greater than the differences that occur
between the observed averages for two or more races within the same major group”

10
There has been a host of special issues and websites set up to address the renewed
interest in the relationship of genomics and race. The journal of the American
Psychological Association American Psychologist released a special issue in January
of 2005, which included sociologists and bioethicists. The Social Sciences Research
Council’s web forum, Is Race “Real”?, came online in the same year, partly in
response to demand by media outlets for information on the subject. Nature Genetics
also published a special supplement in 2004, which was sponsored by the
Department of Energy.

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(UNESCO 1952:12-13). Among population geneticists, there is agreement that the
“bulk” of within genetic variation is about 90-95% (Cooper 2003). Another common
statistic finds eighty-five percent of all human variation can be found in all
populations, while approximately fifteen percent can found between populations
(Fausto-Sterling 2003). Measures of sameness and difference have contributed to
scientists announcing the end of race as a biological concept.
From the contemporary standpoint of genotypes, there is no biological basis
for racial categorization. It would seem that the existence of bio-races could be
adequately debunked by the logic of sameness and within/between. However, as
Cooper writes, "Into this storm of controversy rides genomics" (2003:1166). While
the HGP registered a strong empirical salvo against the validity if bio-race and
scientists largely agree on the within/between position, genomic technologies have
allowed for a closer look at the .1% and the 5-15% of variation between groups.
Genomics has only fueled the debates about population/racial differences and
genome projects, such as HapMap are contributing millions of dollars to research on
them. Debates about the existence of race are not going away anytime soon. They are
only transforming.

Does Race Exist?
I don’t think we should be so politically correct or so afraid and become so
ambiguous and tiptoe around what we’re actually doing. We should just be
transparent, open, about what the nature of the research is that we’re doing,
what the purpose is, what the outcome is. And I think that kind of honesty
and professional integrity is more important than endless, endless, endless
discussions on definitions of whether race exists or not.
(Interview 1008)

167

In biomedical debates about the reality of race as a discrete biological category and
sub-group of humanity there is a strong sentiment that this very discussion is
problematic and “getting in the way” of progress. Weiss and Fullerton (2005)
suggest that this discussion is “getting nowhere” and that the issue continues to go
“’round and ‘round” (165). The data on human variation being produced by current
research in population genetics and genomics is not showing anything new. This
position holds that physical anthropology has long been making the case for one
human race. Further, that there is some utility in using racial groups to understand
health, for instance. Epidemiological data has shown that focusing on race, such as in
research on sickle cell disease, is productive making a sickle cell study in a Japanese
population nonsensical and misguided.
There is a sense among HapMap respondents that fear and anxiety should not
stop such projects from moving forward. While there is general knowledge of the
risks in terms of linking genetics and race, those risks, which may not be tangible in
their eyes, do not outweigh the benefits of the production of scientific knowledge in
the discovery of genetic causes for common diseases. Using groups that correspond
to common sense and historically grounded notions of racial groups is not going
away soon. As a HapMap geneticist and expert in bioethics explained, “I think there
is a general recognition that certain things need to change and we need to do things
in a more careful way. But I don’t think there is really the consciousness about
actually doing it” (Interview 1022). Critics who urge caution in the use of racial
groups in genetic research, and many of them are not scientists and doctors, but

168
social scientists, are deemed naysayers (Rotman 2004). Their concerns often are
relegated to the irrational (fear), emotional (anxiety), and political (impure) rather
than the products of social and historical knowledge. Scientists argue that there are
observable racial differences between populations that require investigation.
“Unfortunately, race is a politically charged topic, and there will be evildoers. But
the fear should not outweigh the benefit of looking” (Burchard in Rotman 2005). The
question remains, when does ‘fear’ become justified? When does one decide not to
do something even though it seems possible and, in fact, may be accomplishable and
could better humanity, even if the costs include reproducing social inequities
I think that the issue is going to be figuring out which differences make a
difference, and for what purpose. And to recognize that it’s a basic human
characteristic, sadly, that many people really focus on trying to found out
ways that they’re better than other people. This is another tool. On the other
hand, treating everybody the same when they’re not runs other sorts of risks.
So I think that any time you focus on the way individuals vary from each
other, you open up a series of questions that can be pretty hard. (Interview
1005)

Both scientist and bioethicist interviewees from HapMap tended to see the
issue of race and subsequent discussions as a distraction when the goal is to develop
therapeutic interventions from genomic research (Interview 1008). Further, some
even suggested that there is a general resentment of the NHGRI directing a set
amount of its operating budget to social science research. A joke amongst scientists
is that ELSI is simply a philosophy class for scientists. The implication in the joke is
that ELSI is, to an extent, public relations and a soft pursuit that is taking resources
away from the real research needs in the hard sciences.

169
While there is much discussion in the scientific literature about what race is
exactly, there is little effort made to define it. As mentioned above, many
acknowledge that it is a social-political category whose origins have dubious
intentions and then go on to use is as a variable for some population group. Race has
no biological basis, but it is constantly used, without an explicit rationale as a
biological category. “The concept of race is as slippery as an eel, and so elusive that
it even eludes itself” (Weiss and Fullerton 2005:168). While scientific research
diminishes the historical record as unjustified fear, the HapMap is an example of the
creep of racial classification back into the mainstream of scientific research. This
classification is justified in the potential benefits that genome projects, such as
HapMap, promise for health. Advocates of color consciousness research argue that
there will be important discoveries to be found by looking at ethnic or racial
differences in medicine. Disparities in health between different racial groups are key
sites to disentangle environmental and genetic factors. Some, such as Esteben
Gonzalez Burchard, a physician and assistant professor of medicine and
biopharmaceutical sciences at the University of California, San Francisco, argue that
it is essential to follow the clues in racial science research (Rotman 2005). There are
others, however, who would rather see race eliminated altogether from clinical and
scientific research.
Advocates of color-blindness argue that race does not matter any longer and
any references to race, whether in diversity hiring policies, college admissions, or the
collection of government statistics, should be eliminated. Society needs to just get
over race. The construction of distinct human sub-groups was a project of

170
enlightenment thinkers and the science of modernity from the seventeenth to
nineteenth centuries. The twentieth century has witnessed a profound intervention in
the construction of race through a deconstruction of not only the category of race
itself, as biological reality, but the process of racial (re)construction. Du Bois’
seminal text, The Souls Black Folk, began this process in 1903.
Currently, there are social and natural scientists that continue to argue for the
utility of race as a meaningful category of human groups or that there are
distinguishable and measurable differences between them. The lineage of racial
classification that is largely credited to Blumenbach (1795) but really began with his
teacher Linneaus (1758) has been carried through the nineteenth and twentieth
centuries via Galton (1869), Darwin’s cousin, Jensen (1969), Herrnstein and Murray
(1994), and Rushton (1995). The fervor from the left and the right of the political
spectrum with the publication of The Bell Curve has died down in the popular press.
However, Philippe Rushton has, almost single handedly, been trying to keep the
discussion of the evolutionary origins of modern racial differences afloat. This, in
spite of threats of dismissal from his academic post, charges of human rights
violations, and having to give his lectures via recorded video tapes due to the
controversy that ensued with the publication of Race, Evolution, and Behavior: A
Life History Perspective. Primarily he argues that, in his terminology,
Mongloids/Orientals, Caucasoids, and Negroids differ in terms of brain and genital
size and a number of other ‘racial’ differences.
11
The three exist on a continuum of

11
The differences Rushton examines include brain size, genital size, rate of sexual
maturation, length of menstrual cycle, frequency of sexual intercourse, gamete

171
human evolution with Africans at the underdeveloped end, East Asians occupying
the overdeveloped end and Europeans playing a mediating role. Rushton may be
avoiding a hierarchy of races where Europeans occupy the dominant position. This
is, however, in alignment with a colorblind racial ideology where whiteness holds
the normative position from which all other races are measured.
Recently, Rushton and Jensen published a review article of race and
intelligence research over the past thirty years (2005). In it, they continue to argue
that culture-only hypotheses of group differences fail to account for differences in IQ
between blacks and whites. Drawing on Herrnstein and Murray, they suggest that
responsible social policies and good science needs to incorporate the extent to which
heredity affects group differences. They suggest that “discrimination policies” or
race-based social policies, such as affirmative action, and “the value of diversity”
(281), that recognize structural inequalities are harming the rights of the individual
and merit reconsideration. Rather than suggesting that racial discrimination has
disappeared, they make the observation that racial harmony is disrupted by the
persistence of discrimination policies. For them, the view that racism is responsible
for social inequality causes mutual resentment between blacks and whites. Overt
racism is a thing of the past and blacks equate their lack of success compared to
whites with a white racism that is invisible. In turn, whites “resent that nonfalsifiable
accusation and the demands to compensate Blacks for harm they do not believe they

production, sexual hormone levels, the tendency to produce dizygotic twins, marital
stability, infant mortality, altruism, law abidingness, and mental health. See
Herrnstein and Murray 1994:642. For a discussion of the link between Herrnstein
and Murray’s and Rushton’s work see Graves 2002:58-61.

172
caused” (282). Racism itself is not the outcome of discriminatory institutions and
everyday racism. Rather, it is now the result of a misunderstanding resulting from
“the view that one segment of the population is largely to blame for the problems of
another segment” (Ibid.). They argue that the burden of proof of group differences
should shift to the culture-only perspective, as “there is too little evidence of any
environmental effects” (279).

De-Racializing the Genome: From Proxy to Precision
When researchers use race as a variable in biomedical research, it is as a proxy for
environmental (culture, diet, class, geographic location) or genetic influences. Used
in this way, race tends to ‘explain’ complex processes that may be unrelated to
racialized identity, thus, re-producing biological notions of race. In scientific
discussions and among HapMap project members some argue that race is still useful
in scientific studies. The ‘correct’ way to employ race is by having a clear, scientific
rationale and definitions.
I don’t think that we should you know invent new words simply because
we’re scared of the old one, and especially when those new words will be
misunderstood and make it scientifically impossible to validate studies cause
we’re not really sure where people came from, what they were meant when
they were using the word race. (Interview 1008)

Many scientists find the use of race as a proxy for populations highly problematic
(Interview 1006). Grouping people by race produces a “disconnect at the molecular
level” (Interview 1006) from the cultural level. How we are grouped politically and
culturally is at odds with how our genes group us together in terms of haplotypes that
code for a particular disease, for example. Genomics can produce the specific of

173
genes at the individual level. This may produce what Rose and Novas (2004) refers
to as “biological citizenship” where new communities are being created based on a
shared relationship to a particular disease and form of treatment. Scientists argue that
genome markers are a “much more refined tool to try to track biology than the crude
gross level of race” (Interview 1006). One of the promises of genomics is to de-
couple the link between the biological and the social that was forged in the rise of
eighteenth and nineteenth century scientific racism. Keita et al (2004) find that
biological variation does not structure individuals into distinct racial groups.
However, by using three “continental groups,” from Africa, Asia, and Europe as
orienting points for genomic studies, it should be no surprise that not only the “lay”
translations through the media but scientists themselves have trouble making the
disconnect between “human genome variation” or “geographical ancestry” and race.
A number of scientists and health researchers suggest that race is a poor proxy for
genomic differences (Havranek and Masoudi 2006).
…alot of our characterizations have been based on the group, and everybody
in the group may not have a given characteristic that’s common in the group.
And when we use these terms interchangeably as we often do in medicine,
that’s where you begin to introduce problems. For example, a gene may have
a high frequency like Sickle Cell. It’s found common in African Americans
but the frequency of the heterozygote is 10 or 12 percent. That means 90 to
88 percent of folk who are equally, quote, “black” don’t have the Sickle gene.
So when we tend to equate the whole with what is common, or what might be
a differential frequency in a group, we got problems when we now have to
deal with the individual level, because when a person comes into the
physician with all the characteristics of an anemic kind of problem, and for
the physician to say, well this person is black so they obviously have Sickle
Cell… But, now the genomics in particular has pushed us to a point in
biology where we need to dissociate equating or using race as a surrogate for
biology. That’s the point we’re making exactly, that we need to at best be
looking at the markers that track with whatever biology you want to study,

174
wherever they fall. Because a black person, and we have examples of this
too, a black person may have Tay Sachs disease and never get diagnosed
because you equate that with Jews! (Interview 1006)

Tay Sachs disease has been shown to have high rates with Ashkenazi Jews and sickle
cell has been associated with African-Americans since the 1970s. Both of these
discoveries have been important in identifying at risk populations for early
screening. However, scientists and medical practitioners are now arguing that race
needs to be disconnected from disease as genomics provides better tools for
identifying disease risk in individuals. Using race or ethnicity as a tool to identify
groups that have proclivities to certain diseases frames out members of other groups
who may be affected. Linking race and ethnicity to disease frames out environmental
factors as well. Winker (2006) argues that new immigrants’ experiences and cultural
practices may be different than long time residents of the US. Despite these possible
differences, both groups are lumped together across racial groups. In the Journal of
Epidemiology and Community Health, Agyemang et al (2005) concur with Winker
and add that difference in socioeconomic status within African Americans is ignored
by using race as a proxy.
In a special issue in Nature Genetics dedicated to “’Race’ and the human
genome,” Royal and Dunston (2004) suggest that the paradigm for genome research
needs to change from race to “human genome variation.” Instead of stratifying
groups by race, individuals can be clustered according to shared patterns of variation
in their DNA. While this strategy shifts the discourse from anthropologic and
biological notions of race, it continues to locate the causes of complex disease in the
genome and neglects to integrate environmental factors. A population geneticist

175
suggests that shifting the paradigm from race to “human genome variation” does not
solve the problem. Substituting one biological term for another is not the
paradigmatic shift that will drag science out of the racial drift it has been entangled
in for over 200 years. The change that needs to be made is from biological
determinism to a more ecological approach that is flexible and recognizes the
outcomes of the specific research questions.
…we don’t really want to change the paradigm from race to human genome
variation. It may be human genome variation. But it also may be diet. It may
be socio-economic status. It may be something else in terms of what specific
variable we are looking at. So, now I am talking about changing from proxy
to precision. And the precision is based on what it is we are trying to find.
(Interview 1022)

There are a number of barriers to moving from proxy to precision, from using a blunt
instrument like race or ethnicity, which some view as an “interim solution” (Duster
2005), to actually measuring and targeting the underlying cause(s) to a person’s
specific health issue. Cultural assumptions about the racial order in scientific
research are clearly one of the barriers. The OMB categories and the regulation of
pubic research forces researchers group people into narrow racial and ethnic
categories (Interview 1022). An international bioethicist suggested there needs to be
changes in “all entities of the research process” (Ibid.) from the granting agencies,
such as the NIH, and how they require grantees to categorize study groups, to the
way institutional review boards review protocols, to the journals and how editors and
reviews look at and review manuscripts. As discussed in the previous chapter, some
journal editors

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…have actually in years past issued, quote un-quote, guidelines about what
researchers need to do and needing researchers to be more specific about why
they use a particular group and how they’re defining this group. I think the
problem is that some of those guidelines have not really been implemented.
And I think unless they are, then people are not going to feel the need to
make that change. I think there has to be a concerted effort to implement the
things that we know we need to do to move this discussion forward. And I
think unless there are consequences. Unless manuscripts are not published,
unless you define your group properly. Then there is not going to be a
change. (Ibid.)

Moving from proxy to precision is a step towards personalized medicine.
Pharmacogenomics and genomics has been aiming at individualizing medical care
with the goal of “the prediction of risk and the treatment of disease on the basis of a
person’s genetic profile – which would render biological considerations of race
obsolete” (Phimster 2003:1082; See also Bamshad 2005). One of the steepest hurdles
to personal medical profiles is the cost of sequencing a genome. The first draft of the
human genome from the Human Genome Project and Celera cost $300 million. This
year, nonhuman genomes have been completed at costs of $22 million and close to
$100,000 (Service 2006). Others cite the current cost to be it he neighborhood of $30
million for a human genome (Willis et al 2005). In 2003, Francis Collins suggested
the future of genome research should be aiming for a $1000 genome (Collins et al
2003). Continuing the competition from the Human Genome Project, Craig Venter’s
Science Foundation offered a reward of $500,000 for meeting that goal and others
have made similar offers. Recently, Venter contributed his reward to the X Prize
Foundation who is offering a prize of $10 million dollars to someone who can map
100 different human genomes in ten days. In spite of these much hyped genome
races, the $1000 genome is considered the “ultimate goal” and represents a shift in

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the technology of sequencing as well as impacting the way in which medicine can be
done. For example, Kreiner (2005) considers the financial impact population wide
screening could have on the health care system. Screening all four million babies
born every year could reduce medical costs by improving disease targeting and
treatment. HapMap participants clearly viewed genome research as a direct route to
individualized medicine (Interviews 1005, 1006, 1008, 1020).

Strategic Essentialism
There appears to be two processes at work in health and biomedical research that, on
first glance, appear to be contradictory. On the one hand, the promise of new
genetics is to uncouple the biological and the social, to de-racialize the human body.
Once and for all, biology has the technological tools to show that racialized
differences are truly skin deep and bare no causal relationship to behavior or
intelligence and are, in fact, socio-political constructs. As the Human Genome
Project found, all of humanity is 99.9% the same. On the other hand, race is being
used as a category for marshalling resources and inclusion in health research and to
address disparities in health. In the 1990s, health became the next wave of civil
rights, following political equality in the 1960s, and economic equality in the 1980s.
Social theorists refer to the process of political mobilization around an identity
category as strategic essentialism.
One interviewee, an African-American geneticist, commented about being
attacked on this issue, “I get a direct question how can you on one hand say, there’s
no biological race, and then you specifically study disease in a particular group?”

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(Interview 1006). This question implies there is a contradiction in claiming that race
is not biological and them studying a particular racial group. I have to admit that I
asked the interviewee a similar question. In a series in Discover Magazine about
genes, race, and medicine, science reporter Jeff Wheelright considers this same
relationship. He writes that the “two positions don’t fit together neatly – at least they
don’t to me, but that’s probably because the social dimensions of race complicate my
view” (2005:Online). What Wheelwright is suggesting is that his perspective, and
mine, is a white point of view. Shifting perspective entails disentangling the two
processes by situating them socially and historically. The interviewee was gently
telling me there is no contradiction as the relationship between the two positions is
historically specific and conjunctural. In the struggle for racial equality, bio-race
needed to be deconstructed. At the same time, race has been useful for minority
groups for political organization and community formation.
As outlined in the previous chapter, public health practitioners and
biomedical researchers began discussing disparities in health and health services in
minority communities in journals in the late 1980s. Color conscious policies in
government-funded research has addressed the issue of discrimination in health care
and health related studies. The NIH Revitalization Act of 1993 sought to bring
diversity to clinical research where most human subjects had been white men. The
act ensured that NIH funded clinical studies must include members of racial minority
groups and women as research subjects and the trials be designed to discern whether
or not the variables being studied affect women or minorities in a different manner
than other subjects (Epstein 2004).

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In the context of a color blind society, race based social policies are at odds
with color blind social policies. The former acknowledges that race and racial
discrimination remain as structuring devices in society that enable some groups
access to resources and restricts others. In terms of health, a race conscious
perspective takes into consideration the history of discrimination of health services
and the environmental factors that lead to disparities of health. The latter, however,
views any racial discrimination as a remnant of the past and not part of the current
social structure. Therefore, there is no need for color conscious programs, such as
affirmative action. For health and biomedical researchers, there is value in using race
to cast one’s net when fishing for a gene. For example, epidemiological data shows
that Type II diabetes occurs more frequently in inner city blacks than in suburban
whites. In the search for the gene that causes Type II diabetes, researchers would
draw from inner city blacks. This is not to say that a researcher could not look for the
same gene in suburban whites, but the choice would not make sound scientific sense,
based on the epidemiological data. According to one of the interview respondents,
recruiting 50% blacks and 50% whites would not enrich such a study (Interview
1006). Further, because humanity began in Africa, African populations have a much
more diverse and more developed genome, compared to groups that migrated out of
Africa, such as Europeans. This means that the haplotype blocks of people of
European decent are much larger than that of people of African decent, such as
African-Americans. European haplotype blocks have not had as long for the DNA to
recombine into smaller blocks. In terms of trying to find a gene,

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I have a yardstick in one case and with another population I’m trying to find a
gene, but my yardstick is divided up into rulers, 12 inch rulers. Now if the
gene is somewhere within the distance of the yardstick, if I find a linkage or
association with that gene in the European population, I just know its
somewhere in this yard. But with blacks, for that same reason because
there’s greater variation, the block sizes are smaller. I can see which, where
does that gene, does it associate with the first 12 inches, or the middle 12
inches, or the end 12, the other end 12 inches. And so I can come closer into
the gene just by tracking markers in this group where the block size is more
refined. (Interview 1006)

While biological determinism frames a causal relationship between the biological
(the independent variable) and the social (the dependent variable), the no race/race
contradiction dissolves if the two variables are arranged in a recursive relationship
and embedded in social and historical context. While arguing the scientific validity
of targeting a particular racial group, this researcher is also facing the history of
marginalizing minority groups from scientific research and clinical trials. At the
same time race is a strategy for marshalling resources and population sampling.
The contradiction between race as biologically invalid and used in scientific
research is one of the great ironies of BiDil, the first race based drug. A
pharmaceutical company such as Nitromed employs a racial strategy in combining
two failed drugs and targeting African-Americans with heart disease. The American
Association of Black Cardiologists not only supported this move, they were co-
sponsors of the AeHeft trials. One the one hand, making a race-specific drug blurs
the boundaries between cultural and biological difference. Cultural factors are easier
to accept in cases of racial disparities in economics or education, for example. The
solutions seem to easily lie in the sphere of the social. Disparities in health along
racial lines have clear roots in the social, such as issues of access to services or early

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detection of cancer and there are major federal initiatives supporting this point of
view. Poverty is clearly a social problem, not a biological one. But using race in
medicine is more complicated than other discussions. There is more slippage into
biological determinism. Further, the commercialization of racialized health problems
and parceling off drugs to this race or that one raises further complications. On the
other hand, minority groups have used race as an organizing strategy to gain political
access and representation. So why should health be any different? To address the
issue of health disparities, race is being employed by the Black Caucus and the
Association of Black Cardiologists as a tool of political organization. When the
cause of disease is deemed biological and not social, the type of difference that is
deployed is intimately related to historical conceptions of race being rooted in
biology and not as the product of social struggles over economics, politics, and
culture. For African American doctors, health care professionals, and scientists, the
double consciousness afforded by the racialized position from which they speak is
both a resource and a restriction (Helmreich 2003).

Colorblind Race Talk in Genomics
Colorblindness advocates the end of over references to race but not the end of
racism. Race talk is often in veiled codes instead of overt statements about racial
minorities. Bonilla-Silva delineates the semantic moves of whiteness that make-up
the stylistic tools of colorblindness (2003:54). He makes a distinction between
everyday conversation where talk is informal, such as among friends, and public
forums where actors are much more careful about what they say. Race talk in new

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genetics takes place largely in scientific and medical journals where the discourse is
polished and the coded language that characterizes color blindness is much more
difficult to pick up in highly educated elites than in everyday stories. One must be
attentive to general frameworks, slippages, and contradictions in the formal
narratives of the journal discourse and the less formal interviews. A recent trend in
scientific literature is the discursive move to colorblind language in human
genomics. There have been a plethora of editorials, letters, research studies, and
commentaries on the subject of race and genomics in scientific, biomedical,
pharmaceutical, and public health journals. Heated discussions have been raging in
the journals about whether or not to use race in research, how to use race, and what
other terms to substitute or employ in an attempt to shift the paradigm from bio-race.
There are even cases where scientists say race is not biologically valid, and then go
ahead and use it as a categorical variable. The usage is uneven at best.
A survey of the scientific literature shows a number of different semantic
moves scientists utilize in trying to reconceptualize human populations in language
that omits race. Terms such as “biogeography of human populations” (Tishkoff and
Kidd 2004), “continentally defined groups” (Burchard 2003). “human genome
variation” (Royal and Dunston 2004), and “ancient geographic ancestry” (Tang et al
2005) are but a sample employed to ‘get past’ race. Keita et al (2004) offer a whole
host of alternatives: ethnoancestral, bioethnic, ethnobiohistorical, ancestral-ethnic,
social-designation, biocultural, biopopulation, ethnosocial, ancestral, ancestor-
historical, origin group, and ethnogeographical. No wonder there is confusion and
disagreement.

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Many studies use common sense labels for populations and some argue for
using the categories on the U.S. census, originating in 1977 as Statistical Policy
Directive No.15 from the Office of Management and Budget. There has been an
equivalent move to use “ethnicity” instead of race as it is viewed as a “broader
construct that takes into consideration cultural tradition, common history, religion,
and often a shared genetic heritage” (Burchard 2003:1171). However, when ‘ethnic’
groups are compared, via the term “geographical ancestry,” they are often from one
of the three major continental/racial groups, Africa, Asia, and Europe. In some cases,
race does not even need to be mentioned. In a commentary in Science, Couzin (2002)
describes controversies surrounding the concept of haplotype blocks and the early
stages of the HapMap project. There are three pictures of faces that are placed
prominently on the first page, in the middle of the text, a boy, a man, and a woman,
of Asian, African, and European decent, respectively. The pictures feature normative
images of these three ethnicities. Nowhere in the text does Couzin mention race or
ethnicity. However, as Fausto-Sterling notes, “it stares out at us nonetheless”
(2003:9). The underlying assumption in genomics is that the whole of humanity is
being studied; the genome is the global human. However, in the pictures, the three
racialized groups represent the human race.
In a response to colorblindness in genomics, a number of papers authored by
Stanford geneticist Neil Risch argue for continuing the use of race, not racial,
categories. Risch and his co-authors argue that “no biological basis for race” and
“race-neutral” approaches do not “derive from objective scientific perspective”
(Risch et al 2002:1). Instead, they advocate for an “objective and scientific (genetic

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and epidemiologic) perspective” that contends there is “great validity in racial/ethnic
self-categorizations” (Ibid). There is little difference between their categories and the
census categories, except that even the census does not use “Caucasian.” The
“evolutionary tree of human races” they provide includes Africans, Caucasians,
Pacific Islanders, East Asians, and Native Americans, looks almost identical to
Blumenbach’s eighteenth century classification scheme. The Out of Africa narrative
has displaced the racial hierarchy theory. Risch et al’s assertion seems to be
motivated by the backlash against the cultural politics of the 1990s. The authors
contend their approach is free of politics and a sober assessment of the “facts” while
any move to disassociate race from biology is politically motivated and ‘politically
correct.’ Bio-race is a tool for understanding the biology of disease and addressing
disease risk. Utilizing data mining techniques, Rosenberg et al (2002), examined
DNA from fifty-two populations in an effort to show that clusters of similar DNA
regions do not reflect an underlying racial structure to humanity. Cooper et al (2003)
directly confront Risch et al’s use of census categories for racial categorization,
arguing that there "is no evidence that the units of interest for medical genetics
correspond to what we call races” (1167). Further, the ‘discovery’ of races in
genomics is not a progressive step for health research, but “an extension of the
atavistic belief that human populations are not just organized, but ordered” (1169).
The massive amounts of data produced by genomics has not “provided evidence that
race can act as a surrogate for genetic constitution in medicine… [and] has not been
shown to provide a useful categorization of genetic information about the response to
drugs, diagnosis or causes of disease” (1168). Risch et al’s response immediately

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follows the Cooper et al article and reasserts the race as biology position (Burchard
et al 2003). The scientists carefully admit that the desire to abandon race is
understandable, especially if one believes that focusing on difference will exacerbate
racial disparities in health. Then they oddly connect the authors in the Cooper article
with the advocates of Prop 54, the conservative Racial Privacy Initiative in the 2004
gubernatorial ballot. This ballot has been referred to as a key strategy of
colorblindness and laissez faire racism. Again, they equate the planetary humanism
position that is against bio-race with the proposal erected by neo-conservative
organizations, including its architect, Ward Connelly, the UC Regent who was key in
the movement to strike down affirmative action in admissions to the University of
California.

The Turn to Racial Realism
Risch et al’s work is typical of the racial realism position that views dissenting
voices against bio-race as politically motivated and scientifically naïve. The racial
realism frame articulates race as biologically determined group differences (See
Sarich and Miele 2004). Some argue for a non-hierarchical racial order while others
maintain racist ideologies of superiority and inferiority. The Bell Curve and
Rushton’s work would align with the latter position. Risch’s views (described above)
are particularly interesting because of his proximity to the mainstream of biomedical
research. Unlike Rushton, another racial realist who is widely shunned across the
academic community, Risch is tenured faculty at Stanford and was an author in the
2004 Nature Genetics Supplement on race and the genome.

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Stanford bioethicist Sandra Lee (personal communication) suggests that we
are moving towards the idea of racial realism. Racial realism comes from the work of
Derrick Bell (1990, 1992) who argued at the end of the 1980s gains from the civil
rights movement were being lost in the fervor of the rising reformist movement.
Brown et al (2003) elaborate on Bell. They argue that racial realists, such as Dinesh
D’Souza (1995) and Shelby Steele (1990), claim that the progress has been made
since the 1950s in addressing racial justice and that racism is a thing of the past.
Contemporary inequalities are not due to white racism, but inactivity on the part of
minorities, and that minority leaders keep racial fervor alive so they can benefit from
government programs (Brown et al 2003:6-7). They do not suggest that racial
discrimination has disappeared, but that discourse about race should reflect the
‘reality’ of biological differences between populations.

“I am a Racially Profiling Doctor”
Sally Satel is an example of a racial realist in the field of health. In a best selling
book and numerous pieces in the press, Satel declares herself to be a racial profiling
doctor (2002a). She borrows a term that social scientists and advocacy groups have
used to highlight discrimination in stop and search procedures by police forces
across the U.S. (See Duster 2004).
In practicing medicine, I am not colorblind. I always take note of my patient's
race. So do many of my colleagues. We do it because certain diseases and
treatment responses cluster by ethnicity. Recognizing these patterns can help
us diagnose disease more efficiently and prescribe medications more
effectively. When it comes to practicing medicine, stereotyping often works.
(Satel 2002a: 56)

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Like Risch, Satel is noticeably oppositional to “political correctness,” which she
argues has its roots in what she, incorrectly, refers to as Foucault and postmodernism
(2002b). For Satel, race is not a burden, but an individual characteristic where it is
not an acknowledgment of racism per se, but someone of a particular race could have
a certain drug response or disease. Race is a proxy for risk. Satel argues that the state
and public health has become too involved in the doctor’s task to deliver medical
solutions to patients. Because of the way that political correctness has “infected” the
academy and even the natural sciences and medicine, health has become a means for
addressing social problems. Satel’s position cascades into ideas about who is
responsible for addressing the roots of disease.
For racial realism, race is no longer a burden but an individual characteristic.
There is not an acknowledgement of racism per se, but that different races could
have a genetic allele that could have a drug response or a particular disease. Race is
an individual problem rather than a social phenomenon. When doctors such as Satel
profile, they reduce race to an individual characteristic. At the same time when
health is becoming a genetic phenomenon, rather than a biological phenomenon, it is
no longer a distributive justice issue or social justice issue that the state becomes
implicated in. This raises questions about who is responsible for social ills such as
health disparities among minority groups and personal ills, such as cancer. Since
there is no longer structural discrimination in society, goes this position, and any
disparities are individual cases of lack of will, cultural differences, and bad choices.
Color-conscious policies are not needed as the conditions they were meant to address
have been alleviated. When health becomes a genetic phenomenon, rather than a

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biological one combined with environmental causes, the state is no longer implicated
in issues of distributive justice. Health becomes something that the individual has to
deal with through pharmacogenomics or individualized medicine to address one’s
difference, not the state (Lee 2003).
In terms of the debates about group differences and the ‘reality’ of race, a
disquieting situation looms on the horizon. One of the scientists who has been
particularly outspoken about the focus on race in HapMap suggested that ongoing
discussions about racialization in genomics is regressive and that such people who
suggest HapMap and other projects can and will reproduce race are naysayers.
However, the same scientists offered a future implication of genomics. He suggested
to me, “the rubber has not really hit the road yet.” For him, the ancestral information
that may be contained in DNA is a superficial issue. Genome maps of human
populations are not productive of or re-producing the scientific racism of the past or
reifying race. For him, knowing where your mother came from through your
mitochondria does not say much except what our geographical origins may be. This
may be true if one removes social and historical context of that information.
However, as this dissertation argues, such knowledge contributes to and is produced
in discursive formations of the informationalization of race. The interviewee
continued,
But what is going to be problematic and we’re not prepared to deal with is
what if, or not what if because it will happen, what about when someone does
find a gene that does affect a trait of high interest that’s not medical perhaps
but behavioral, and that gene variant is not equally represented across
different ethnic groups? All of sudden we won’t be in this confused sort of

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meaningless realm we’ve been in to some extent. We’re going to be in a new
realm where there are genes that effect traits and they’re not evenly
distributed across populations. Then we’re going to have a real problem.
(Interview 1001)

This particular problem is (re)surfacing at a time when science, technology, and race
are interacting in new ways.

Misinterpretation of the Data
Scientists are aware of the problems with studying human groups and the history of
racial categorization in population genetics. Mainstream discussions in all areas of
the biomedical sciences and public health center around the utility of using race as a
categorical variable and suggestions for alternative concepts. Science, Nature, the
Journal of the American Medical Association, the New England Journal of Medicine,
Genome Biology, and Genomics have all featured editorials, letters, research studies,
and commentary on the subject of race and science. Scientists and medical
researchers make concerted efforts to distinguish population groups from racial
groups. Either by using geographically oriented concepts, such as “geographic
ancestry,” specific names for groups, or indicating where race is used to indicate
environmental rather than biological factors (Bamshad 2005). This is especially the
case in research targeting disease. For the most part, the majority of scientists and
doctors believe that these efforts avoid producing racial meaning into genetic
research and elude historical biases. Interview respondents suggest that findings from
population studies become racialized through misinterpretation of the data. For them,
the key interpretation of population studies for the general population comes from

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the media (Interview 1001). For example, a bioethicist gave the case of an article by
Rosenberg et al (2002) that studied genetic variation in samples taken from a wide
range of population throughout the world (Interview 1004). The researchers
categorized the groups by genetic similarity and “were careful not to use the word
race to describe the populations that they found were genetically similar to each
other” (Ibid.). However, when the results entered the public sphere, she noted, “race
very prominently used in the lay reporting of that particular research” (Ibid).
While the media do sensationalizes stories, such as calling BiDil the first
race-based drug or black drug (Malik 2005), Risch (2006) cites the same study as
evidence that genetic clusters do align with racial groups. Sometimes researchers
mix up race and ethnicity in examining group differences in health issue. For
example, Haiman et al (2006) investigate differences between African-American,

Japanese-American, Latino, Native Hawaiian, and white men and

women in their
Multiethnic Cohort Study of the differences in rates of lung cancer from cigarette
smoking. There are similar “black/white studies all over the place” (Interview 1006).
Because of the technological advances in the last 10 years, there has become an
astonishing capacity for gene mapping and typing. As discussed in Chapter Two, the
cost for high throughput has declined dramatically making big science projects, such
as HapMap, much more feasible economically. The existence of this technological
infrastructure has enabled scientists to do certain types of comparative analyses
simply because they can. Studies that compare geographically disparate groups and
tend to follow an African/European/Asian model, may not be theoretically sound.
Lee suggests that “researchers feel little pressure to be explicit about the meaning

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and significance of racial and ethnic identity in framing their research hypotheses
(2005: 2136). A bioethicist suggested that we are seeing more of these types of
studies being published because of the existence of three difference data sets from
three different people being fed into a computer (Interview 1017). When the surface
of the hypothesis is scraped, however, the assumptions guiding the study are
grounded in a particular view of the world. For example, Rosenberg et al (2002; not
the Rosenberg above) compare MTHFR C677T polymorphism frequencies between
three groups, whites, Japanese, and Africans. The authors use a mix of racial,
national, and continental identity markers. Interestingly, the ‘white’ population is
referred to as Israeli and Arab. Said (1978) has well documented the racialization of
the Oriental Other in opposition to the white European. Also, there is no rationale for
comparing the groups. However, the authors are from Israel, Japan, and Ghana. The
increase in racial group comparisons, compared to the 1990s comes at a time when
journals have been installing guidelines for submissions of research that employs
racial categories (see Chapter 3). The scientist’s claim of inaccuracy may be
unfounded. Bubela and Caulfield (2004) analyzed press reporting of scientific papers
and found that the majority of the articles had high levels of accuracy (See also
Condit 2004).

Conclusion
In this chapter, I attempted to disentangle the biological and social by situating the
discourse of science in larger social discourses. I identified four frames that
characterize current race talk in the information age and showed how they operate in

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genomics in order to show how cultural discourse is embedded in the way that
scientists and doctors discuss different populations, design research, and offer
solutions to health problems. While scientific research is built on a position of
neutrality from the object of study, it is impossible to study race without common
sense understandings of racial difference ‘infecting’ the objectivity of science.
Scientists’ work is formed both by their own research/theoretical traditions and
cultural resources. The mix of political perspectives and scientific rationales for or
against the use of race in biomedical research reveals a state of uncertainty and
disagreement in the medical and biological sciences.
One of the key features of the current discursive terrain is the seeming
equivalence between conservative and progressive positions on certain issues. The
racial realism frame is juxtaposed with the use of race as a strategy to assemble
resources for minority communities or include marginalized groups. Both deploy
race as a primary category if human identity, but for very different purposes.
Researchers such as Risch argue that when groups are statistically aggregated, they
match up with the racial categories set out in the US census. This lends mainstream
legitimacy to scholars such as Rushton who continue to not only argue that there are
racial groups, but they differ in ways that perpetuate ideologies of scientific racism.
At the same time, organizations such as the Association of Black Cardiologists use
race to draw attention to racialized health disparities and the production of
pharmaceutical interventions.

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Thus far, the precision tools of genome science have not been successful in
disarticulating race and biology. The original UNESCO statements on race
articulated the position that race has no biological basis over half a century ago.
Recently, there has been a renewed commitment to this position in the American
Anthropological Association’s response to the OMB census categories and the joint
findings of the Human Genome Projects, among many others. However, the specter
of race continues to shroud genomic research into human difference. Rather than
race being solely a social concept or a biological one, they become entangled in one
another.

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Chapter 6
Conclusion

As a mode of representation, a structuring device, and as a biological category, race
is undergoing a significant transformation. The informationalization of race
conceptualizes this change, which lies at the articulation of new media, the digital
network age, and colorblindness. Race as information differs from race as the body,
culture, or nation due to the transformations and arrangement of organizational,
institutional, and discursive infrastructures. While this process can be observed
across social institutions, such as marketing and law enforcement, genetic
engineering has emerged as a key technology in the informationalization of race.
Rather than destroying race as a biological category and providing conclusive proof
that we are indeed all the same, as the UNESCO statements in the 1950s tried to
establish and leaders of the Human Genome Project proclaimed, the specter of
genetic differences that distinguish human groups and their behavior has not only
been raised once again, but given legitimacy. The context for this gene war is not
slavery and colonialization in the eighteenth century, or scientific racism in the
nineteenth century, or the eugenics movement across the west with its grossest
expression in Hitler Germany in the first half of the twentieth century. The
informationalization of race emerged within the rise of multiculturalism and the
ideology of colorblindness, new communication technologies, and biotechnology
and health.

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The HapMap Project and human genomics has provided a case study of how
the informationalization of race has developed in a specific institution through the
use and shaping of technology, legal and institutional change, and cultural
representation. Biology’s transformation into an information science has been
facilitated by innovations in computing science and communication technologies.
While the new technological paradigm was taking shape from the 1970s onwards,
biology was undergoing its own revolution. The discovery of rDNA in 1973 enabled
genetic engineering. DNA science, however, increasingly demanded new types of
data collection and analysis as well as the automation of sequencing techniques.
Recently, genome projects have not only incorporated technological transformations,
they have motivated them. The image of the lone scientist laboring over a
microscope and a Petri dish has given way to global big science projects featuring a
consortium of geneticists, doctors, biologists, statisticians, bioinformaticians,
lawyers, and social scientists. Databases have been integral to the management of
genetic information. Terabytes of data are collected, stored in local labs, uploaded to
global databases such as dbSNP, the Single Nucleotide Polymorphism database at
the National Institutes of Health in Bethesda, Maryland. While the Human Genome
Project claimed that we are all 99.9 percent the same, the HapMap project has been
creating a database that compares the differences in genomic variation between
racialized groups.
The Internet provides the means of mobility not only for the DNA data, but
also for HapMap to operate in real time in multiple sites on four continents. The
Internet also increases the internal communication of the project as well its external

196
communication to the larger community of interested scientists and biotechnology
companies. As a number of HapMap members expressed, genome projects would not
be possible without the Internet. While the Internet has increased communication
within science, it has also enabled patients and research subjects to play a more
participatory role in the doctor-patient and researcher-subject relationships. HapMap
participants pointed to these features of the Internet as a process of democratizing
scientific and biomedical research. However, the democratizing effect is far from
international. Developing nations also need to be able to build technological
infrastructures that enable their scientists to adequately connect to the Internet. The
haplotype maps that result from the HapMap Project are not global if they can only
be accessed and used by scientists from technologically advanced nations. While
technological advancement has been a central process in the growth and
reorganization of biology, the disciplinary practices have also been shaped by
changes in government regulation, law, and journal policies.
The material birth of genetic engineering and the maturation of biology as a
scientific discipline also prompted a shift from academic based research to private
enterprise. In chapter four, I highlighted a number of the key changes in the
regulatory landscape of the emerging biotechnology industry and scientific research
from 1977 to 2004 that enable and constrain HapMap and genomic research. The
Chakrabarty case and the Baye-Dole and Stevenson-Wydler bills in 1980
encouraged the deregulation of university-industrial relations and industrialization of
biology. The increased interaction between industry and academia produced an
intellectual climate weary of privately funded research submitted to journals that

197
could compromise the integrity of pure science. Throughout the 1980s and 1990s,
journals became the forums for the debates about conflict of interest policies. A
number of leading journals began to require authors to disclose privately funded
research. Finally, an obscure mandate from the US Office of Management and
Budget in 1977 set forth the racial classification for the US census that continues to
be the standard in publicly funded biomedical research. When President Clinton
signed the NIH Revitalization Act in 1993, ensuring that women and minorities
must be included in biomedical studies, it drew on the OMB categories, Black,
White, Asian-Pacific Islander, and Native American as the measure for diversity.
HapMap participants commented that the employment of this standard is a major
reason why many genome studies, including HapMap, compare white, Asian, and
African continental groups. The NIH Act followed calls from doctors, researchers,
and advocacy groups to increase diversity in biomedical research. These discussions
emerged in the 1980s in the US and the UK and also focused on the the reproduction
of racism in the reporting of racial and ethnic groups. The British Medical Journal
was the first major biomedical journal to publish guidelines for the reporting and use
of race and ethnicity. The guidelines stated that doctors and scientists should be
careful of the terms they use to describe different groups to avoid reproducing
common sense stereotypes. By the 2000s, similar guidelines had been adopted
unevenly across biomedical and scientific journals. Those editors who did often
required researchers to state their rationales for the use of race as a variable. The
discussions about ethnic and racial reporting and inclusion in biomedical research
echoed larger debates about how race should be used in scientific research.

198
Finally, scientific discourse about race has its own history and specialization.
Even though the scientific ethos is built on a position of neutrality from the object of
study and a distance between science and society, cultural assumptions about the
nature of race and the social order are deeply embedded in scientific discourses of
race. Popular understandings of racial difference ‘infect’ the purity of science. While
race becomes negotiated through the writing of computer codes such as complex
algorithms in data mining, race talk operates through coded language that has
become common sense since scholars began writing about the new racism in the
1980s. Overt references to racial hierarchies or outright racist statements have given
way to a complex of references about the nature of difference, social inequality, and
the allocation of societal resources. Biological claims of group superiority and
inferiority have been replaced by discourses of cultural difference. This research
began from the position that race talk operated through the dominant racial paradigm
that has emerged since the 1970s, colorblindness. However, I found that racial claims
making not only operates through the dominant ideology of colorblindness but
through three additional discursive frames, strategic essentialism, racial realism, and
planetary humanism. Instead of finding the legacy of scientific racism or the
decoupling of race and biology in genomics, I found a confusion of complicated
positions about the definition of race, its role in biomedical research, its relationship
to biological or geographical ancestral groups, and its use to mobilize health
resources. Technologically, institutionally, and discursively, the informationalization
of race has been produced through a much more complex set of positions about
racial difference than in previous eras.

199

Race and the Work of Information in the Age of the Digital Database
The informationalization of race foregrounds the relational nature of racialization
between social groups (Kim 1999). A race relations framework, which characterizes
much of the work on race and racism in the last century, and racial formation (Omi
and Winant 1994) and racialization (Small 1999) perspectives often focus on the
relationship between a particular minority group and the dominant white culture.
Both approaches to the study of the racial order in the US tend to focus on the
black/white binary. The informationalization of race follows a racialization approach
to the extent that it traces emerging processes across social institutions and the
relationship between structure and representation. The informationalization of race
extends past work on race by placing technological change and innovation at the
center of analysis (Gray 2005; Nakamura 2002). Race as information is a product of
the digital age.
In the digital age, processes of creating, storing, and transmitting information
become specific resources for production and power. The policies, politics, and
procedures that make up information infrastructures are hidden in the seeming
neutrality of codes and standards. We, the users, only see the interface. Information
and the networks that comprise a communication infrastructure are much more than
the denotative transmission of facts, computer algorithms, and data (Bowker and Star
1999). Information is not simply descriptive and reflective of the social world; the
collection, storage, and analysis of information constitutes what is known and what
can be imagined, both the basis for social action. Information in the global new

200
economy is deeply connotative. In this respect, race as information works in a similar
manner to race as culture and race as (the epidermal) body. However, due to the
hypertext of the Internet and the anti-narrative logic of databases, information is
much more contingent and fluid (Manovich 1999). The end of Jim Crow signaled a
retreat from a society structured in dominance. The subsequent rise of
multiculturalism and post modernity loosened the moorings of racial identity. While
structural inequalities persisted, the post civil rights era and the information age
produced a contested field of racial meanings in representation, politics, and the
biomedical sciences.
Genomics has emerged as the newest and possibly most powerful contributor
to the debate about the existence of biologically distinct human races. On the one
hand, the Human Genome Project concluded that we are all basically the same at the
molecular level. However, whole genome projects, such as the HapMap Project,
conceptualize human groups in such a manner as to highlight differences and
similarities. Black, white, and Asian groups have been shown to differ in their
configuration and arrangement of haplotype blocks. Further, HapMap data showed
Chinese and Japanese groups to be so alike that they merged the two groups. While
the scientific rationale for these groupings and the resulting amalgamation of the Han
Chinese and Tokyo Japanese samples is accepted amongst scientists, the symbolic
meaning needs to be placed in the context of both scientific and popular discourse
about race. What is not confronted in the scientific literature is the entire
phenomenon of migration, first, in the placements and displacements of people
during the era of European expansion and, second, in the period of de-colonization

201
since World War II and globalization. Subsequent phases of human genomics will
look at other groups and critics of the sample populations have argued that choosing
groups that are so far apart geographically exaggerates difference. But what the
initial three “continental groups” does is set up a base line, a normative position from
which all others will be measured. Additionally, there appears to be little space to
discuss the place of new ethnicities, whether they arise from migration or from
practices of racial ‘mixing’. Admixture tends to reinforce a discourse of racial purity.
Finally, there are a number of scholars who address the history of race and science
(Gould 1994; Gilman 1985), the role of history, politics, and ethics in the Human
Genome Diversity Project (Reardon 2005), how genetic data is racialized in labs
(Fullwiley Forthcoming), race and pharmacogenomics (Lee 2003), and return of
eugenics (Duster 2003). However, they all pass over the fundamental role of ICTs in
the development of new genetics.
New genetic technologies have been particularly important in the
racialization of information. They are the newest and most powerful tools for
constructing differences between human groups. Filling human skulls with mustard
seed or grape shot in the eighteenth century was highly unreliable in comparing
differences between racial groups (not to mention the fact that some scientists fudged
their data to put whites at the top of the human hierarchy. See: Gould 1996), but
allele frequencies, SNP variation, and data mining techniques are perceived as much
more precise. Contemporary biology has both motivated developments in
automation, data mining, and communication technologies and re-organized itself
around an information paradigm borrowed from computing science. The bulk of a

202
genome scientist’s time is no longer looking through a microscope, but pouring
through genetic data and re-imagining the order and function of human DNA.
Scientists and genome project organizers argue that their discoveries will help in
further understanding human health and the genetic origins of disease. At the same
time, genetic technologies are being developed to build racialized databases, such as
state databanks in the US and the UK. ICT’s have been integral to the racialization of
information and the rise of genetic technologies. At the heart of this transformation
are the Internet and databases.
Information technologies based around the Internet and other networks have
become commonplace. In an age of blogs, MySpace, and YouTube, database
technologies that make up technological and informational infrastructures usually do
not take center stage in public discourse and research agendas on new media. Yet,
they are ubiquitous and their functionality is weaved throughout the circuits of the
information economy. When linked in internal networks, such as the FBI’s CODIS
DNA database, or through the Internet, databases become powerful tools for
surveillance and social sorting of populations. Database technologies have been
innovating along with advances in the speed and capacity of computing. There is
little work on the role, uses, development, and meaning making function of databases
in society. With the increasing proliferation of databases, further research is crucial
to compliment the growing body of knowledge about the Internet.
Many have argued that the Internet, with its hypertext architecture, is
transforming the way we think. At the interface of web users and their mouse, this
may very well be the case. However, I would suggest looking further into the net’s

203
cyberinfrastructure where database technologies provide the source of data
collection, storage, and analysis. The collection and archiving of information is
characteristic of the modern practices of collective memory and social control in the
management of societies. Digital technology, however, has transformed the
processes of managing information and its form. Old media, from the printing press
to film and television, largely worked in linear terms, from producer to audience,
from beginning of the text to the end of the text. In McLuhanite terms, the narrative
is the message. Media studies has shown that audiences do not accept media
messages passively, but sometimes actively decode the text within a limited number
of frames. With digitization and database technologies, the linearity of traditional
media has been disrupted by the anti-narrative logic of the database. First, the sheer
amount of content that can be stored in databases has reached terabyte levels and
continues to grow. Second, with the aid of the Internet, digital information can be
transmitted and accessed across the globe with the right equipment and, depending
on the nature of the database, the right access key. Central repositories, networked
through open access protocols over the Internet or over private intranets, enable real
time access and also the ability of users to feed information back into the database.
Finally, digital information in databases can be searched and arranged in different
ways, depending on the goals of the user. In the case of genomics research, data
mining enables tasks that would have previously been too cumbersome, time
consuming, or expensive.

204
The Informationalization of Race: A Cultural Theory of Technology and
Identity

The goals of this dissertation are two-fold. First, I wanted to draw attention to
emerging trends in the relationship between technology and race. The second goal is
to empirically observe and describe these trends in the context of the information age
and colorblindness. The informationalization of race theorizes the racialization of
technology and information in the digital age. While the innovations of new
communication technologies induce this transformation in the construction of
difference, this development has emerged under the conditions of political economic,
organizational, and cultural changes. I have employed the HapMap Project and
human genomics as a case study and an indicator of these changes.
Biotechnology is a form of new media. I argued that communication theory
should pay more attention to the role of biomedia and that the tools of
communication policy could be applied to the political economy of the
biotechnology industry. Both the media and biotechnology provide sites that raise
questions about political economy and power in organizations. Policy oriented
communication theory largely focuses on the political economy of the
telecommunications industry in the US, other national contexts, and, increasingly, as
a force of globalization. I found that there was a concurrent deregulation of the
biotechnology industry and telecommunications industry in the 1980s followed by
the regulation of identity in the turn to diversity in the 1990s. Both industries raise
important issues of identity, new technologies, and social inclusion.

205
Technology has never been a ‘cure’ for social ills. The inception of
technology and its uses are entirely dependent on the work of social actors and
institutions. This means that the virtual space of the Internet is not the end of raced
identities, or gendered ones, in real life. As research has shown, practices of race and
gender are at the same time transcribed and transcended into cyberspace as virtual
space is deeply connected to real space. Further, the neutrality of data mining
technologies is also dependent on the nature of the data and the algorithms that tell
programs what to do. The standards and codes that make up the design infrastructure
of databases and related technologies shape their function and meaning at the user
interface. In the case of genome and DNA databases, race is inscribed into the front
and back ends of the technology. Further, DNA databases have become part of the
surveillance network of information societies.
Finally, scholars of communication and race need to address the role of new
technologies and the information age in the process of racialization. The methods
and theories of critical race scholarship have diversified and grown to well address
the ways in which people of color are making opportunities out of digital media.
New media forms are developing at a dizzying pace and are crucial tools for
interventions into dominant forms of representation. While, genetic technologies are
being used as forms of social control, scientists at the Human Genome Center at
Howard University who are leaders in the field of genome research and members of
the HapMap project are also shaping them.

206
The need for theoretical connections between technology and race from
observations of the biomedical science is particularly important at this time. With the
turn to difference in genomics, the 0.01 percent that differentiates one person from
another amounts to about 30 million base pairs. Those points of difference are under
intense scrutiny as governments and companies create public and private haplotype
maps. While the public projects operate under the ethos of open access to
democratize the data and community consent, private firms are under no obligation
to adopt such policies as well as journal policies on the use and reporting of race and
ethnicity. This should be cause for concern, especially as the mapping of DNA turns
to the function of DNA. In order to avoid returning to the altar of biological
determinism, debates about genomics and race need to be expanded to incorporate
technology beyond simply its function. Like other sorts of information that are used
to determine access to health care, employment, and insurance, genomic data will be
used to sort people. Communication technologies create new possibilities not only in
what can be done, but how we see the social and scientific world.

207
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Appendix A

Models of Racial Discourse

Figure 1. Traditional Race Talk

Figure 2. Central Frames of The Informationalization of Race

Race
Conservative

Progressive

No Race

Strategic Essentialism

Racial Realism

Planetary Humanism

Colorblindness

Anti-Racism
Progressive
Racism
Conservative

234
Appendix B

Code for Interviews

Interview 1001 Population geneticist,
committee chair, site
principle investigator
7 September 2005 Telephone
Interview 1002 Biostatistician 2 June 2005 Telephone
Interview 1003 Project manager 9 June 2005 Telephone
Interview 1004 Bioethicist 13 June 2005 Telephone
Interview 1005 Bioethicist, committee chair 27 July 2005 Telephone
Interview 1006 Microbiologist, site PI 6 October 2005 Telephone
Interview 1007 Director of NGO, site PI 7 May 2005 Telephone
Interview 1008 Lawyer, bioethicist, site PI 20 June 2005 Telephone
Interview 1009 Bioinformatician 10 June 2005 Email
Interview 1010 Population geneticist 10 June 2005 Telephone
Interview 1011 Bioethicist, site PI 22 September
2005
Telephone
Interview 1012 Medical geneticist, site PI 21 June 2005 Email
Interview 1013 Geneticist 11 July 2005 Telephone
Interview 1014 Human geneticist 11 July 2005 Telephone
Interview 1015 Biochemist 7 October 2005 Telephone
Interview 1016 Population geneticist, site PI 25 May 2005 Telephone
Interview 1017 Bioethicist 22 June 2005 Telephone
Interview 1018 Biologist 6 September 2005 Email
Interview 1019 Staff scientist 14 September
2005
Telephone
Interview 1020 Statistical geneticist 17 October 2005 Telephone
Interview 1021 Human Geneticist, site PI 17 October 2005 Telephone
Interview 1022 Human Geneticist,
bioethicist
11 November
2005
Telephone
Interview 1023 Bioinformatician 22 September
2005
Telephone
Interview 1024 Microbiologist 10 May 2005 Telephone
Interview 2001 Biologist, senior scientist 12 May 2005 Biotechnology
company
Interview 2002 Biologist, senior scientist 24 August 2005 Biotechnology
company

Telephone and on-site interviews were digitally recorded, stored on a computer, and
backed up on a CD. Even though the interviews were recorded, I also took notes to
help guide questions in the interview and for reflection post-interview. When the
interview was completed, I would go over the notes and make annotations for issues
and items that could be addressed in subsequent interviews and/or analysis. After
having them transcribed, I checked the transcripts against the recording for accuracy.

235
I developed a coding schedule (which was constantly being refined). Then, I used a
qualitative software program, Nvivo to code the interviews. I coded the three sub-
sections first, technology, university-industrial relations, and race. Further coding
was necessary until the response had been categorized sufficiently, usually no more
than three levels. After identifying a broad number of sub-codes, I refined them,
merging similar codes and eliminating some if there were less then three responses to
the code. In creating a report outline, I looked at what was said, how it corresponded
to the literature in the area (or not), and then tried to develop report sections that
would correspond to both the literature and data.

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Asset Metadata

Creator Chow-White, Peter A. (author)

Core Title The informationalization of race: communication technologies and genomics in the information age

School Annenberg School for Communication

Degree Doctor of Philosophy

Degree Program Communication

Publication Date 08/07/2007

Defense Date 12/10/2006

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag culture,genomics,health,information society,new media,OAI-PMH Harvest,policy,race

Language English

Advisor Castells, Manuel (committee chair), Sanchez, George J. (committee member), Sturken, Marita (committee member)

Creator Email petercw@sfu.ca

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m645

Unique identifier UC1121326

Identifier etd-ChowWhite-20070807 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-529680 (legacy record id),usctheses-m645 (legacy record id)

Legacy Identifier etd-ChowWhite-20070807.pdf

Dmrecord 529680

Document Type Dissertation

Rights Chow-White, Peter A.

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu