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An Introduction to Science
and Technology Studies
Praise for the first edition
“This book is a wonderful tool with which to think.
It offers an expansive introduction to the field of science
studies, a rich exploration of the theoretical terrains it
comprises and a sheaf of well-reasoned opinions that
will surely inspire argument.”
Geoffrey C. Bowker, University of California, San Diego
“Sismondo’s Introduction to Science and Technology Studies, . . .
for anyone of whatever age and background starting
out in STS, must be the first-choice primer: a resourceful,
enriching book that will speak to many of the successes,
challenges, and as-yet-untackled problems of science studies.
If the introductory STS course you teach does not fit
his book, change your course.”
Jane Gregory, ISIS, 2007
An Introduction to Science
and Technology Studies
This second edition first published 2010
© 2010 Sergio Sismondo
Edition history: Blackwell Publishing Ltd (1e, 2004)
Blackwell Publishing was acquired by John Wiley & Sons in February 2007. Blackwell’s
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Library of Congress Cataloging-in-Publication Data
An introduction to science and technology studies / Sergio Sismondo. – 2nd ed.
Includes bibliographical references and index.
ISBN 978-1-4051-8765-7 (pbk. : alk. paper)
1. Science–Philosophy. 2. Science–Social aspects. 3. Technology–Philosophy.
4. Technology–Social aspects. I. Title.
A catalogue record for this book is available from the British Library.
Set in 10/12.5pt Galliard by Graphicraft Limited, Hong Kong
Printed in Singapore
The Prehistory of Science and Technology Studies
The Kuhnian Revolution
Questioning Functionalism in the Sociology of Science
Stratification and Discrimination
The Strong Programme and the Sociology of Knowledge
The Social Construction of Scientific and Technical Realities
Feminist Epistemologies of Science
Two Questions Concerning Technology
10 Studying Laboratories
12 Standardization and Objectivity
13 Rhetoric and Discourse
14 The Unnaturalness of Science and Technology
15 The Public Understanding of Science
16 Expertise and Public Participation
17 Political Economies of Knowledge
Science & Technology Studies (STS) is a dynamic interdisciplinary field, rapidly
becoming established in North America and Europe. The field is a result
of the intersection of work by sociologists, historians, philosophers, anthropologists, and others studying the processes and outcomes of science,
including medical science, and technology. Because it is interdisciplinary, the
field is extraordinarily diverse and innovative in its approaches. Because it
examines science and technology, its findings and debates have repercussions
for almost every understanding of the modern world.
This book surveys a group of terrains central to the field, terrains that
a beginner in STS should know something about before moving on. For
the most part, these are subjects that have been particularly productive
in theoretical terms, even while other subjects may be of more immediate
practical interest. The emphases of the book could have been different, but
they could not have been very different while still being an introduction
to central topics in STS.
An Introduction to Science and Technology Studies should provide an
overview of the field for any interested reader not too familiar with STS’s
basic findings and ideas. The book might be used as the basis for an upperyear undergraduate, or perhaps graduate-level, course in STS. But it might
also be used as part of a trajectory of more focused courses on, say, the social
study of medicine, STS and the environment, reproductive technologies,
science and the military, or science and public policy. Because anybody putting
together such courses would know how those topics should be addressed
– or certainly know better than does the author of this book – these topics
are not addressed here.
However the book is used, it should almost certainly be alongside a
number of case studies, and probably alongside a few of the many articles
mentioned in the book. The empirical examples here are not intended to
replace rich detailed cases, but only to draw out a few salient features. Case
studies are the bread and butter of STS. Almost all insights in the field grow
out of them, and researchers and students still turn to articles based on
cases to learn central ideas and to puzzle through problems. The empirical
examples used in this book point to a number of canonical and useful studies.
There are many more among the references to other studies published in
English, and a great many more in English and in other languages that are
This second edition makes a number of changes. The largest is reflected
in a tiny adjustment of abbreviation. In the first edition, the field’s name
was abbreviated S&TS. The ampersand was supposed to emphasize the
field’s name as Science and Technology Studies, rather than Science, Technology, and Society, the latter of which was generally known as STS in
the 1970s and 1980s. When the ampersand seemed important, the two
STSs differed considerably in their approaches and subject matters: Science
and Technology Studies was a philosophically radical project of understanding science and technology as discursive, social, and material activities;
Science, Technology, and Society was a project of understanding social
issues linked to developments in science and technology, and how those
developments could be harnessed to democratic and egalitarian ideals.
When the first edition of this book was written, the ampersand seemed
valuable to identifying its terrain. However, the fields of STS (with or without ampersand) have expanded so rapidly that the two STSs have blended
together. The first STS (with ampersand) became increasingly concerned with
issues about the legitimate places of expertise, about science in public
spheres, about the place of public interests in scientific decision-making. The
other STS (without) became increasingly concerned with understanding
the dynamics of science, technology, and medicine. Thus, many of the most
exciting works have joined what would once have been seen as separate.
This edition, then, increases attention to work being done on the politics
of science and technology, especially where STS treats those politics in more
theoretical and general terms. As a result, the public understanding of
science, democracy in science and technology, and political economies of
knowledge each get their own chapters in this edition, expanding the scope
of the book.
Besides this large change, there is considerable updating of material from
the first edition, and there are some reorganizations. In particular, the chapter on feminist epistemologies of science has been brought forward, to put
it in better contact with the chapters on social constructivism and the strong
programme. The four chapters on laboratories, controversies, objectivity, and
creating order have been reorganized into three.
I hope that these additions and changes make the book more useful
to students and teachers of STS than was the first. It is to all teachers and
students in the field, and especially my own, that I dedicate this book.
The Prehistory of Science
and Technology Studies
A View of Science
Let us start with a common picture of science. It is a picture that coincides
more or less with where studies of science stood some 50 years ago, that
still dominates popular understandings of science, and even serves as something like a mythic framework for scientists themselves. It is not perfectly
uniform, but instead includes a number of distinct elements and some healthy
debates. It can, however, serve as an excellent foil for the discussions that
follow. At the margins of science, and discussed in the next section, is technology, typically seen as simply the application of science.
In this picture, science is a formal activity that creates and accumulates
knowledge by directly confronting the natural world. That is, science makes
progress because of its systematic method, and because that method allows
the natural world to play a role in the evaluation of theories. While the
scientific method may be somewhat flexible and broad, and therefore may
not level all differences, it appears to have a certain consistency: different
scientists should perform an experiment similarly; scientists should be able
to agree on important questions and considerations; and most importantly,
different scientists considering the same evidence should accept and reject
the same hypotheses. The result is that scientists can agree on truths about
the natural world.
Within this snapshot, exactly how science is a formal activity is open. It
is worth taking a closer look at some of the prominent views. We can start
with philosophy of science. Two important philosophical approaches within
the study of science have been logical positivism, initially associated with
the Vienna Circle, and falsificationism, associated with Karl Popper. The
Vienna Circle was a group of prominent philosophers and scientists who
met in the early 1930s. The project of the Vienna Circle was to develop a
philosophical understanding of science that would allow for an expansion
Prehistory of Science and Technology Studies
of the scientific worldview – particularly into the social sciences and into
philosophy itself. That project was immensely successful, because positivism
was widely absorbed by scientists and non-scientists interested in increasing
the rigor of their work. Interesting conceptual problems, however, caused
positivism to become increasingly focused on issues within the philosophy
of science, losing sight of the more general project with which the movement began (see Friedman 1999; Richardson 1998).
Logical positivists maintain that the meaning of a scientific theory (and
anything else) is exhausted by empirical and logical considerations of what
would verify or falsify it. A scientific theory, then, is a condensed summary
of possible observations. This is one way in which science can be seen as
a formal activity: scientific theories are built up by the logical manipulation of observations (e.g. Ayer 1952 ; Carnap 1952  ), and
scientific progress consists in increasing the correctness, number, and range
of potential observations that its theories indicate.
For logical positivists, theories develop through a method that transforms
individual data points into general statements. The process of creating
scientific theories is therefore an inductive one. As a result, positivists tried
to develop a logic of science that would make solid the inductive process
of moving from individual facts to general claims. For example, scientists
might be seen as creating frameworks in which it is possible to uniquely
generalize from data (see Box 1.1).
Positivism has immediate problems. First, if meanings are reduced to
observations, there are many “synonyms,” in the form of theories or statements that look as though they should have very different meanings but
do not make different predictions. For example, Copernican astronomy was
initially designed to duplicate the (mostly successful) predictions of the
earlier Ptolemaic system; in terms of observations, then, the two systems were
roughly equivalent, but they clearly meant very different things, since one
put the Earth in the center of the universe, and the other had the Earth
spinning around the Sun. Second, many apparently meaningful claims are
not systematically related to observations, because theories are often too abstract
to be immediately cashed out in terms of data. Yet surely abstraction does
not render a theory meaningless. Despite these problems and others, the
positivist view of meaning taps into deep intuitions, and cannot be entirely
Even if one does not believe positivism’s ideas about meaning, many
people are attracted to the strict relationship that it posits between theories
and observations. Even if theories are not mere summaries of observations,
they should be absolutely supported by them. The justification we have
for believing a scientific theory is based on that theory’s solid connection
Prehistory of Science and Technology Studies 3
Box 1.1 The problem of induction
Among the asides inserted into the next few chapters are a number of
versions of the “problem of induction.” These are valuable background for
a number of issues in Science and Technology Studies (STS). At least as stated
here, these are theoretical problems that only occasionally become practical
ones in scientific and technical contexts. While they could be paralyzing in
principle, in practice they do not come up. One aspect of their importance,
then, is in finding out how scientists and engineers contain these problems,
and when they fail at that, how they deal with them.
The problem of induction arose with David Hume’s general questions
about evidence in the eighteenth century. Unlike classical skeptics, Hume
was interested not in challenging particular patterns of argument, but in
showing the fallibility of arguments from experience in general. In the sense
of Hume’s problem, induction extends data to cover new cases. To take a
standard example, “the sun rises every 24 hours” is a claim supposedly established by induction over many instances, as each passing day has added
another data point to the overwhelming evidence for it. Inductive arguments take n cases, and extend the pattern to the n+1st. But, says Hume,
why should we believe this pattern? Could the n+1st case be different,
no matter how large n is? It does no good to appeal to the regularity of
nature, because the regularity of nature is at issue. Moreover, as Ludwig
Wittgenstein (1958) and Nelson Goodman (1983 ) show, nature
could be perfectly regular and we would still have a problem of induction.
This is because there are many possible ideas of what it would mean for
the n+1st case to be the same as the first n. Sameness is not a fully defined
It is intuitively obvious that the problem of induction is insoluble. It is
more difficult to explain why, but Karl Popper, the political philosopher
and philosopher of science, makes a straightforward case that it is. The problem is insoluble, according to him, because there is no principle of induction that is true. That is, there is no way of assuredly going from a finite
number of cases to a true general statement about all the relevant cases.
To see this, we need only look at examples. “The sun rises every 24 hours”
is false, says Popper, as formulated and normally understood, because in
Polar regions there are days in the year when the sun never rises, and days
in the year when it never sets. Even cases taken as examples of straightforward and solid inductive inferences can be shown to be wrong, so why
should we be at all confident of more complex cases?
Prehistory of Science and Technology Studies
to data. Another view, then, that is more loosely positivist, is that one
can by purely logical means make predictions of observations from scientific
theories, and that the best theories are ones that make all the right predictions. This view is perhaps best articulated as falsificationism, a position
developed by (Sir) Karl Popper (e.g. 1963), a philosopher who was once
on the edges of the Vienna Circle.
For Popper, the key task of philosophy of science is to provide a demarcation criterion, a rule that would allow a line to be drawn between science
and non-science. This he finds in a simple idea: genuine scientific theories
are falsifiable, making risky predictions. The scientific attitude demands that
if a theory’s prediction is falsified the theory itself is to be treated as false.
Pseudo-sciences, among which Popper includes Marxism and Freudianism,
are insulated from criticism, able to explain and incorporate any fact. They
do not make any firm predictions, but are capable of explaining, or explaining
away, anything that comes up.
This is a second way in which science might be seen as a formal activity.
According to Popper, scientific theories are imaginative creations, and there
is no method for creating them. They are free-floating, their meaning not
tied to observations as for the positivists. However, there is a strict method
for evaluating them. Any theory that fails to make risky predictions is ruled
unscientific, and any theory that makes failed predictions is ruled false.
A theory that makes good predictions is provisionally accepted – until
new evidence comes along. Popper’s scientist is first and foremost skeptical,
unwilling to accept anything as proven, and willing to throw away anything that runs afoul of the evidence. On this view, progress is probably
best seen as the successive refinement and enlargement of theories to cover
increasing data. While science may or may not reach the truth, the process
of conjectures and refutations allows it to encompass increasing numbers
Like the central idea of positivism, falsificationism faces some immediate
problems. Scientific theories are generally fairly abstract, and few make
hard predictions without adopting a whole host of extra assumptions (e.g.
Putnam 1981); so on Popper’s view most scientific theories would be
unscientific. Also, when theories are used to make incorrect predictions,
scientists often – and quite reasonably – look for reasons to explain away
the observations or predictions, rather than rejecting the theories. Nonetheless, there is something attractive about the idea that (potential) falsification
is the key to solid scientific standing, and so falsificationism, like logical
positivism, still has adherents today.
For both positivism and falsificationism, the features of science that make
it scientific are formal relations between theories and data, whether through
Prehistory of Science and Technology Studies 5
Box 1.2 The Duhem–Quine thesis
The Duhem–Quine thesis is the claim that a theory can never be conclusively tested in isolation: what is tested is an entire framework or a web
of beliefs. This means that in principle any scientific theory can be held in
the face of apparently contrary evidence. Though neither of them put the
claim quite this baldly, Pierre Duhem and W.V.O. Quine, writing in the beginning and middle of the twentieth century respectively, showed us why.
How should one react if some of a theory’s predictions are found to be
wrong? The answer looks straightforward: the theory has been falsified, and
should be abandoned. But that answer is too easy, because theories never
make predictions in a vacuum. Instead, they are used, along with many other
resources, to make predictions. When a prediction is wrong, the culprit
might be the theory. However, it might also be the data that set the stage
for the prediction, or additional hypotheses that were brought into play,
or measuring equipment used to verify the prediction. The culprit might
even lie entirely outside this constellation of resources: some unknown object
or process that interferes with observations or affects the prediction.
To put the matter in Quine’s terms, theories are parts of webs of belief.
When a prediction is wrong, one of the beliefs no longer fits neatly into
the web. To smooth things out – to maintain a consistent structure – one
can adjust any number of the web’s parts. With a radical enough redesign
of the web, any part of it can be maintained, and any part jettisoned. One
can even abandon rules of logic if one needs to!
When Newton’s predictions of the path of the moon failed to match the
data he had, he did not abandon his theory of gravity, his laws of motion,
or any of the calculating devices he had employed. Instead, he assumed that
there was something wrong with the observations, and he fudged his data.
While fudging might seem unacceptable, we can appreciate his impulse:
in his view, the theory, the laws, and the mathematics were all stronger
than the data! Later physicists agreed. The problem lay in the optical
assumptions originally used in interpreting the data, and when those were
changed Newton’s theory made excellent predictions.
Does the Duhem–Quine thesis give us a problem of induction? It shows
that multiple resources are used (not all explicitly) to make a prediction,
and that it is impossible to isolate for blame only one of those resources
when the prediction appears wrong. We might, then, see the Duhem–
Quine thesis as posing a problem of deduction, not induction, because it
shows that when dealing with the real world, many things can confound
neat logical deductions.
Prehistory of Science and Technology Studies
the rational construction of theoretical edifices on top of empirical data
or the rational dismissal of theories on the basis of empirical data. There
are analogous views about mathematics; indeed, formalist pictures of science
probably depend on stereotypes of mathematics as a logical or mathematical
But there are other features of the popular snapshot of science. These
formal relations between theories and data can be difficult to reconcile with
an even more fundamental intuition about science: Whatever else it does,
science progresses toward truth, and accumulates truths as it goes. We can
call this intuition realism, the name that philosophers have given to the claim
that many or most scientific theories are approximately true.
First, progress. One cannot but be struck by the increases in precision of
scientific predictions, the increases in scope of scientific knowledge, and the
increases in technical ability that stem from scientific progress. Even in a field
as established as astronomy, calculations of the dates and times of astronomical
events continue to become more precise. Sometimes this precision stems
from better data, sometimes from better understandings of the causes of those
events, and sometimes from connecting different pieces of knowledge.
And occasionally, the increased precision allows for new technical ability or
Second, truths. According to realist intuitions, there is no way to understand the increase in predictive power of science, and the technical ability
that flows from that predictive power, except in terms of an increase of truth.
That is, science can do more when its theories are better approximations
of the truth, and when it has more approximately true theories. For the
realist, science does not merely construct convenient theoretical descriptions
of data, or merely discard falsified theories: When it constructs theories or
other claims, those generally and eventually approach the truth. When it
discards falsified theories, it does so in favor of theories that better approach
Real progress, though, has to be built on more or less systematic methods.
Otherwise, there would only be occasional gains, stemming from chance or
genius. If science accumulates truths, it does so on a rational basis, not through
luck. Thus, realists are generally committed to something like formal relations
between data and theories.
Turning from philosophy of science, and from issues of data, evidence,
and truth, we see a social aspect to the standard picture of science. Scientists
are distinguished by their even-handed attitude toward theories, data, and
each other. Robert Merton’s functionalist view, discussed in Chapter 3,
dominated discussions of the sociology of science through the 1960s.
Merton argued that science served a social function, providing certified
knowledge. That function structures norms of scientific behavior, those
Prehistory of Science and Technology Studies 7
Box 1.3 Underdetermination
Scientists choose the best account of data from among competing hypotheses. This choice can never be logically conclusive, because for every
explanation there are in principle an indefinitely large number of others
that are exactly empirically equivalent. Theories are underdetermined by the
empirical evidence. This is easy to see through an analogy.
Imagine that our data is the collection of points in the graph on the
left (Figure 1.1). The hypothesis that we create to “explain” this data is
some line of best fit. But what line of best fit? The graph on the right shows
two competing lines that both fit the data perfectly.
Clearly there are infinitely many more lines of perfect fit. We can do
further testing and eliminate some, but there will always be infinitely many
more. We can apply criteria like simplicity and elegance to eliminate some
of them, but such criteria take us straight back to the first problem of induction: how do we know that nature is simple and elegant, and why should we
assume that our ideas of simplicity and elegance are the same as nature’s?
When scientists choose the best theory, then, they choose the best
theory from among those that have been seriously considered. There is
little reason to believe that the best theory so far considered, out of the
infinite numbers of empirically adequate explanations, will be the true one.
In fact, if there are an infinite number of potential explanations, we could
reasonably assign to each one a probability of zero.
The status of underdetermination has been hotly debated in philosophy of
science. Because of the underdetermination argument, some philosophers
(positivists and their intellectual descendants) argue that scientific theories
should be thought of as instruments for explaining and predicting, not as
true or realistic representations (e.g. van Fraassen 1980). Realist philosophers,
however, argue that there is no way of understanding the successes of science
without accepting that in at least some circumstances evaluation of the evidence leads to approximately true theories (e.g. Boyd 1984; see Box 6.2).
Prehistory of Science and Technology Studies
norms that tend to promote the accumulation of certified knowledge. For
Merton, science is a well-regulated activity, steadily adding to the store of
On Merton’s view, there is nothing particularly “scientific” about the
people who do science. Rather, science’s social structure rewards behavior
that, in general, promotes the growth of knowledge; in principle it also
penalizes behavior that retards the growth of knowledge. A number of other
thinkers hold that position, such as Popper (1963) and Michael Polanyi
(1962), who both support an individualist, republican ideal of science, for
its ability to progress.
Common to all of these views is the idea that standards or norms are
the source of science’s success and authority. For positivists, the key is that
theories can be no more or less than the logical representation of data.
For falsificationists, scientists are held to a standard on which they have to
discard theories in the face of opposing data. For realists, good methods
form the basis of scientific progress. For functionalists, the norms are the
rules governing scientific behavior and attitudes. All of these standards or
norms are attempts to define what it is to be scientific. They provide ideals
that actual scientific episodes can live up to or not, standards to judge between
good and bad science. Therefore, the view of science we have seen so far
is not merely an abstraction from science, but is importantly a view of
A View of Technology
Where is technology in all of this? Technology has tended to occupy a
secondary role, for a simple reason: it is often thought, in both popular and
academic accounts, that technology is the relatively straightforward application of science. We can imagine a linear model of innovation, from basic
science through applied science to development and production. Technologists
identify needs, problems, or opportunities, and creatively combine pieces
of knowledge to address them. Technology combines the scientific method
with a practically minded creativity.
As such, the interesting questions about technology are about its effects:
Does technology determine social relations? Is technology humanizing
or dehumanizing? Does technology promote or inhibit freedom? Do
science’s current applications in technologies serve broad public goals?
These are important questions, but as they take technology as a finished
product they are normally divorced from studies of the creation of particular technologies.
Prehistory of Science and Technology Studies 9
If technology is applied science then it is limited by the limits of scientific
knowledge. On the common view, then, science plays a central role in determining the shape of technology. There is another form of determinism that
often arises in discussions of technology, though one that has been more
recognized as controversial. A number of writers have argued that the state
of technology is the most important cause of social structures, because technology enables most human action. People act in the context of available
technology, and therefore people’s relations among themselves can only be
understood in the context of technology. While this sort of claim is often
challenged – by people who insist on the priority of the social world over
the material one – it has helped to focus debate almost exclusively on the
effects of technology.
Lewis Mumford (1934, 1967) established an influential line of thinking
about technology. According to Mumford, technology comes in two varieties.
Polytechnics are “life-oriented,” integrated with broad human needs and
potentials. Polytechnics produce small-scale and versatile tools, useful for
pursuing many human goals. Monotechnics produce “mega machines” that
can increase power dramatically, but by regimenting and dehumanizing.
A modern factory can produce extraordinary material goods, but only if
workers are disciplined to participate in the working of the machine. This
distinction continues to be a valuable resource for analysts and critics of
technology (see, e.g., Franklin 1990, Winner 1986).
In his widely read essay “The Question Concerning Technology” (1977
), Martin Heidegger develops a similar position. For Heidegger,
distinctively modern technology is the application of science in the service
of power; this is an objectifying process. In contrast to the craft tradition
that produced individualized things, modern technology creates resources,
objects made to be used. From the point of view of modern technology,
the world consists of resources to be turned into new resources. A technological worldview thus produces a thorough disenchantment of the world.
Through all of this thinking, technology is viewed as simply applied
science. For both Mumford and Heidegger modern technology is shaped
by its scientific rationality. Even the pragmatist philosopher John Dewey (e.g.
1929), who argues that all rational thought is instrumental, sees science
as theoretical technology (using the word in a highly abstract sense) and
technology (in the ordinary sense) as applied science. Interestingly, the view
that technology is applied science tends toward a form of technological
determinism. For example, Jacques Ellul (1964) defines technique as “the
totality of methods rationally arrived at and having absolute efficiency (for
a given stage of development)” (quoted in Mitcham 1994: 308). A society
that has accepted modern technology finds itself on a path of increasing
Prehistory of Science and Technology Studies
efficiency, allowing technique to enter more and more domains. The view
that a formal relation between theories and data lies at the core of science
informs not only our picture of science, but of technology.
Concerns about technology have been the source of many of the movements critical of science. After the US use of nuclear weapons on Hiroshima
and Nagasaki in World War II, some scientists and engineers who had
been involved in developing the weapons began The Bulletin of the Atomic
Scientists, a magazine alerting its readers about major dangers stemming from
the military and industrial technologies. Starting in 1955, the Pugwash Conferences on Science and World Affairs responded to the threat of nuclear
war, as the United States and the Soviet Union armed themselves with nuclear
Science and the technologies to which it contributes often result in very
unevenly distributed benefits, costs, and risks. Organizations like the Union
of Concerned Scientists, and Science for the People, recognized this uneven
distribution. Altogether, the different groups that made up the Radical Science
Movement engaged in a critique of the idea of progress, with technological
progress as their main target (Cutliffe 2000).
Parallel to this in the academy, “Science, Technology and Society” became,
starting in the 1970s, the label for a diverse group united by progressive
goals and an interest in science and technology as problematic social institutions. For researchers on Science, Technology and Society the project of
understanding the social nature of science has generally been seen as continuous with the project of promoting a socially responsible science (e.g.
Ravetz 1971; Spiegel-Rösing and Price 1977; Cutliffe 2000). The key issues
for Science, Technology and Society are about reform, about promoting
disinterested science, and about technologies that benefit the widest populations. How can sound technical decisions be made democratically (Laird
1993)? Can and should innovation be democratically controlled (Sclove 1995)?
To what extent, and how, can technologies be treated as political entities
(Winner 1986)? Given that researchers, knowledge, and tools flow back and
forth between academia and industry, how can we safeguard pure science
(Dickson 1988; Slaughter and Leslie 1997)? This is the other “STS,” which
has played a major role in Science and Technology Studies, the former being
both an antecedent of and now a part of the latter.
A Preview of Science and Technology Studies
Science and Technology Studies (STS) starts from an assumption that
science and technology are thoroughly social activities. They are social in that
Prehistory of Science and Technology Studies 11
scientists and engineers are always members of communities, trained into
the practices of those communities and necessarily working within them. These
communities set standards for inquiry and evaluate knowledge claims. There
is no abstract and logical scientific method apart from evolving community
norms. In addition, science and technology are arenas in which rhetorical
work is crucial, because scientists and engineers are always in the position
of having to convince their peers and others of the value of their favorite
ideas and plans – they are constantly engaged in struggles to gain resources
and to promote their views. The actors in science and technology are also
not mere logical operators, but instead have investments in skills, prestige,
knowledge, and specific theories and practices. Even conflicts in a wider
society may be mirrored by and connected to conflicts within science and
technology; for example, splits along gender, race, class, and national lines
can occur both within science and in the relations between scientists and
STS takes a variety of anti-essentialist positions with respect to science and
technology. Neither science nor technology is a natural kind, having simple
properties that define it once and for all. The sources of knowledge and
artifacts are complex and various: there is no privileged scientific method
that can translate nature into knowledge, and no technological method that
can translate knowledge into artifacts. In addition, the interpretations of knowledge and artifacts are complex and various: claims, theories, facts, and objects
may have very different meanings to different audiences.
For STS, then, science and technology are active processes, and should
be studied as such. The field investigates how scientific knowledge and
technological artifacts are constructed. Knowledge and artifacts are human
products, and marked by the circumstances of their production. In their most
crude forms, claims about the social construction of knowledge leave no role
for the material world to play in the making of knowledge about it. Almost
all work in STS is more subtle than that, exploring instead the ways in which
the material world is used by researchers in the production of knowledge.
STS pays attention to the ways in which scientists and engineers attempt
to construct stable structures and networks, often drawing together into
one account the variety of resources used in making those structures and
networks. So a central premise of STS is that scientists and engineers use
the material world in their work; it is not merely translated into knowledge
and objects by a mechanical process.
Clearly, STS tends to reject many of the elements of the common view
of science. How and in what respects are the topics of the rest of this book.
The Kuhnian Revolution
Thomas Kuhn’s The Structure of Scientific Revolutions (1970, first published
in 1962) challenged the dominant popular and philosophical pictures of the
history of science. Rejecting the formalist view with its normative stance,
Kuhn focused on the activities of and around scientific research: in his work
science is merely what scientists do. Rejecting steady progress, he argued
that there have been periods of normal science punctuated by revolutions.
Kuhn’s innovations were in part an ingenious reworking of portions of the
standard pictures of science, informed by rationalist emphases on the power
of ideas, by positivist views on the nature and meaning of theories, and by
Ludwig Wittgenstein’s ideas about forms of life and about perception. The
result was novel, and had an enormous impact.
One of the targets of The Structure of Scientific Revolutions is what is known
(since Butterfield 1931) as “Whig history,” history that attempts to construct
the past as a series of steps toward (and occasionally away from) present views.
Especially in the history of science there is a temptation to see the past through
the lens of the present, to see moves in the direction of what we now believe
to be the truth as more rational, more natural, and less needing of causal
explanation than opposition to what we now believe. But since events must
follow their causes, a sequence of events in the history of science cannot be
explained teleologically, simply by the fact that they represent progress. Whig
history is one of the common buttresses of too-simple progressivism in the
history of science, and its removal makes room for explanations that include
more irregular changes.
According to Kuhn, normal science is the science done when members of
a field share a recognition of key past achievements in their field, beliefs about
which theories are right, an understanding of the important problems of
the field, and methods for solving those problems. In Kuhn’s terminology,
scientists doing normal science share a paradigm. The term, originally
referring to a grammatical model or pattern, draws particular attention to
The Kuhnian Revolution 13
Box 2.1 The modernity of science
Many commentators on science have felt that it is a particularly modern
institution. By this they generally mean that it is exceptionally rational, or
exceptionally free of local contexts. While science’s exceptionality in either
of these senses is contentious, there is a straightforward sense in which
science is, and always has been, modern. As Derek de Solla Price (1986 )
has pointed out, science has grown rapidly over the past three hundred years.
In fact, by any of a number of indicators, science’s growth has been steadily
exponential. Science’s share of the US gross national product has doubled
every 20 years. The cumulative number of scientific journals founded has
doubled every 15 years, as has the membership in scientific institutes, and
the number of people with scientific or technical degrees. The numbers of
articles in many sub-fields have doubled every 10 years. These patterns
cannot continue indefinitely – and in fact have not continued since Price
did his analysis.
A feature of this extremely rapid growth is that between 80 and 90 percent of all the scientists who have ever lived are alive now. For a senior
scientist, between 80 and 90 percent of all the scientific articles ever
written were written during his or her lifetime. For working scientists the
distant past of their fields is almost entirely irrelevant to their current research,
because the past is buried under masses of more recent accomplishments.
Citation patterns show, as one would expect, that older research is considered
less relevant than more recent research, perhaps having been superseded
or simply left aside. For Price, a “research front” in a field at some time can
be represented by the network of articles that are frequently cited. The front
continually picks up new articles and drops old ones, as it establishes new
problems, techniques, and solutions. Whether or not there are paradigms
as Kuhn sees them, science pays most attention to current work, and little
to its past. Science is modern in the sense of having a present-centered outlook, leaving its past to historians.
Rapid growth also gives science the impression of youth. At any time, a
disproportionate number of scientists are young, having recently entered
their fields. This creates the impression that science is for the young, even
though individual scientists may make as many contributions in middle
age as in youth (Wray 2003).
The Kuhnian Revolution
a scientific achievement that serves as an example for others to follow. Kuhn
also assumes that such achievements provide theoretical and methodological tools for further research. Once they were established, Newton’s
mechanics, Lavoisier’s chemistry, and Mendel’s genetics each structured
research in their respective fields, providing theoretical frameworks for and
models of successful research.
Although it is tempting to see it as a period of stasis, normal science is
better viewed as a period in which research is well structured. The theoretical
side of a paradigm serves as a worldview, providing categories and frameworks into which to slot phenomena. The practical side of a paradigm serves
as a form of life, providing patterns of behavior or frameworks for action.
For example, Lavoisier’s ideas about elements and the conservation of mass
formed frameworks within which later chemists generated further ideas.
The importance he attached to measurement instruments, and the balance
in particular, shaped the work practices of chemistry. Within paradigms research
goes on, often with tremendous creativity – though always embedded in firm
conceptual and social backdrops.
Kuhn talks of normal science as puzzle-solving, because problems are to be
solved within the terms of the paradigm: failure to solve a problem usually
reflects badly on the researcher, rather than on the theories or methods of
the paradigm. With respect to a paradigm, an unsolved problem is simply
an anomaly, fodder for future researchers. In periods of normal science the paradigm is not open to serious question. This is because the natural sciences, on
Kuhn’s view, are particularly successful at socializing practitioners. Science
students are taught from textbooks that present standardized views of fields
and their histories; they have lengthy periods of training and apprenticeship;
and during their training they are generally asked to solve well-understood
and well-structured problems, often with well-known answers.
Nothing good lasts forever, and that includes normal science. Because
paradigms can only ever be partial representations and partial ways of dealing with a subject matter, anomalies accumulate, and may eventually start
to take on the character of real problems, rather than mere puzzles. Real
problems cause discomfort and unease with the terms of the paradigm, and
this allows scientists to consider changes and alternatives to the framework;
Kuhn terms this a period of crisis. If an alternative is created that solves some
of the central unsolved problems, then some scientists, particularly younger
scientists who have not yet been fully indoctrinated into the beliefs and
practices or way of life of the older paradigm, will adopt the alternative.
Eventually, as older and conservative scientists become marginalized, a
robust alternative may become a paradigm itself, structuring a new period
of normal science.
The Kuhnian Revolution 15
Box 2.2 Foundationalism
Foundationalism is the thesis that knowledge can be traced back to firm
foundations. Typically those foundations are seen as a combination of
sensory impressions and rational principles, which then support an edifice
of higher-order beliefs. The central metaphor of foundationalism, of a building firmly planted in the ground, is an attractive one. If we ask why we
hold some belief, the reasons we give come in the form of another set of
beliefs. We can continue asking why we hold these beliefs, and so on. Like
bricks, each belief is supported by more beneath it (there is a problem here
of the nature of the mortar that holds the bricks together, but we will ignore
that). Clearly, the wall of bricks cannot continue downward forever; we do
not support our knowledge with an infinite chain of beliefs. But what lies
at the foundation?
The most plausible candidates for empirical foundations are sense
experiences. But how can these ever be combined to support the complex
generalizations that form our knowledge? We might think of sense experiences, and especially their simplest components, as like individual data points.
Here we have the earlier problems of induction all over again: as we have
seen, a finite collection of data points cannot determine which generalizations to believe.
Worse, even beliefs about sense impressions are not perfectly secure. Much
of the discussion around Kuhn’s The Structure of Scientific Revolutions (1970
 ) has focused on his claim that scientific revolutions change what
scientists observe (Box 2.3). Even if Kuhn’s emphasis is wrong, it is clear
that we often doubt what we see or hear, and reinterpret it in terms of
what we know. The problem becomes more obvious, as the discussion of
the Duhem–Quine thesis (Box 1.2) shows, if we imagine the foundations to
be already-ordered collections of sense impressions.
On the one hand, then, we cannot locate plausible foundations for the
many complex generalizations that form our knowledge. On the other hand,
nothing that might count as a foundation is perfectly secure. We are best
off to abandon, then, the metaphor of solid foundations on which our knowledge sits.
According to Kuhn, it is in periods of normal science that we can most
easily talk about progress, because scientists have little difficulty recognizing
each other’s achievements. Revolutions, however, are not progressive, because
they both build and destroy. Some or all of the research structured by the
The Kuhnian Revolution
pre-revolutionary paradigm will fail to make sense under the new regime;
in fact Kuhn even claims that theories belonging to different paradigms are
incommensurable – lacking a common measure – because people working
in different paradigms see the world differently, and because the meanings
of theoretical terms change with revolutions (a view derived in part from
positivist notions of meaning). The non-progressiveness of revolutions
and the incommensurability of paradigms are two closely related features
of the Kuhnian account that have caused many commentators the most
If Kuhn is right, science does not straightforwardly accumulate knowledge, but instead moves from one more or less adequate paradigm to another.
This is the most radical implication found in The Structure of Scientific
Revolutions: Science does not track the truth, but creates different partial
views that can be considered to contain truth only by people who hold those
Kuhn’s claim that theories within paradigms are incommensurable has a
number of different roots. One of those roots lies in the positivist picture
of meaning, on which the meanings of theoretical terms are related to observations they imply. Kuhn adopts the idea that the meanings of theoretical terms
depend upon the constellation of claims in which they are embedded. A change
of paradigms should result in widespread changes in the meanings of key
terms. If this is true, then none of the key terms from one paradigm would
map neatly onto those of another, preventing a common measure, or even
Secondly, in The Structure of Scientific Revolutions, Kuhn takes the notion
of indoctrination quite seriously, going so far as to claim that paradigms even
shape observations. People working within different paradigms see things
differently. Borrowing from the work of N. R. Hanson (1958), Kuhn argues
there is no such thing, at least in normal circumstances, as raw observation.
Instead, observation comes interpreted: we do not see dots and lines in
our visual fields, but instead see more or less recognizable objects and
patterns. Thus observation is guided by concepts and ideas. This claim
has become known as the theory-dependence of observation. The theorydependence of observation is easily linked to Kuhn’s historical picture,
because during revolutions people stop seeing one way, and start seeing another
way, guided by the new paradigm.
Finally, one of the roots of Kuhn’s claims about incommensurability is
his experience as an historian that it is difficult to make sense of past scientists’ problems, concepts, and methods. Past research can be opaque, and
aspects of it can seem bizarre. It might even be said that if people find it
too easy to understand very old research in present terms they are probably
The Kuhnian Revolution 17
doing some interpretive violence to that research – Isaac Newton’s physics
looks strikingly modern when rewritten for today’s textbooks, but looks much
less so in its originally published form, and even less so when the connections between it and Newton’s religious and alchemical research are drawn
(e.g. Dobbs and Jacob 1995). Kuhn says that “In a sense that I am unable
to explicate further, the proponents of competing paradigms practice their
trades in different worlds” (1970 : 150).
The case for semantic incommensurability has attracted a considerable
amount of attention, mostly negative. Meanings of terms do change, but
they probably do not change so much and so systematically that claims in
which they are used cannot typically be compared. Most of the philosophers,
linguists, and others who have studied this issue have come to the conclusion that claims for semantic incommensurability cannot be sustained, or
even that it is impossible (Davidson 1974) to make sense of such radical
change in meaning (see Bird 2000 for an overview).
This leaves the historical justification for incommensurability. That problems, concepts, and methods change is uncontroversial. But the difficulties
that these create for interpreting past episodes in science can be overcome
– the very fact that historical research can challenge present-centered interpretations shows the limits of incommensurability.
Claims of radical incommensurability appear to fail. In fact, Kuhn quickly
distanced himself from the strongest readings of his claims. Already by
1965 he insisted that he meant by “incommensurability” only “incomplete
communication” or “difficulty of translation,” sometimes leading to
“communication breakdown” (Kuhn 1970a). Still, on these more modest
readings incommensurability is an important phenomenon: even when
dealing with the same subject matter, scientists (among others) can fail to
If there is no radical incommensurability, then there is no radical division
between paradigms, either. Paradigms must be linked by enough continuity of concepts and practices to allow communication. This may even
be a methodological or theoretical point: complete ruptures in ideas or
practices are inexplicable (Barnes 1982). When historians want to explain
an innovation, they do so in terms of a reworking of available resources.
Every new idea, practice, and object has its sources; to assume otherwise is
to invoke something akin to magic. Thus many historians of science have
challenged Kuhn’s paradigms by showing the continuity from one putative
paradigm to the next.
For example, instruments, theories, and experiments change at different
times. In a detailed study of particle detectors in physics, Peter Galison (1997)
shows that new detectors are initially used for the same types of experiments
The Kuhnian Revolution
and observations as their immediate predecessors had been, and fit into
the same theoretical contexts. Similarly, when theories change, there is no
immediate change in either experiments or instruments. Discontinuity in one
realm, then, is at least generally bounded by continuity in others. Science
gains strength, an ad hoc unity, from the fact that its key components rarely
change together. Science maintains stability through change by being disunified, like a thread as described by Wittgenstein (1958): “the strength
of the thread does not reside in the fact that some one fibre runs through
its whole length, but in the overlapping of many fibres.” If this is right then
the image of complete breaks between periods is misleading.
Box 2.3 The theory-dependence of observation
Do people’s beliefs shape their observations? Psychologists have long
studied this question, showing how people’s interpretations of images are
affected by what they expect those images to show. Hanson and Kuhn took
the psychological results to be important for understanding how science
works. Scientific observations, they claim, are theory-dependent.
For the most part, philosophers, psychologists, and cognitive scientists agree
that observations can be shaped by what people believe. There are substantial
disagreements, though, about how important this is for understanding
science. For example, a prominent debate about visual illusions and the extent
to which the background beliefs that make them illusions are plastic (e.g.
Churchland 1988; Fodor 1988) has been sidelined by a broader interpretation
of “observation.” Scientific observation has been and is rarely equivalent
to brute perception, experienced by an isolated individual (Daston 2008).
Much scientific data is collected by machine, and then is organized by
scientists to display phenomena publicly (Bogen and Woodward 1992). If
that organization amounts to observation, then it is straightforward that
observation is theory-dependent.
Theory and practice dependence is broader even than that: scientists attend
to objects and processes that background beliefs suggest are worth looking at, they design experiments around theoretically inspired questions, they
remember relevance and communicate relevant information, where relevance
depends on established practices and shared theoretical views (Brewer and
The Kuhnian Revolution 19
Incommensurability: Communicating Among
Claims about the incommensurability of scientific paradigms raise general
questions about the extent to which people across boundaries can
In some sense it is trivial that disciplines (or smaller units, like specialties)
are incommensurable. The work done by a molecular biologist is not
obviously interesting or comprehensible to an evolutionary ecologist or a
neuropathologist, although with some translation it can sometimes become
so. The meaning of terms, ideas, and actions is connected to the cultures
and practices from which they stem. Disciplines are “epistemic cultures” that
may have completely different orientations to their objects, social units of
knowledge production, and patterns of interaction (Knorr Cetina 1999).
However, people from different areas interact, and as a result science
gains a degree of unity. We might ask, then, how interactions are made
Simplified languages allow parties to trade goods and services without
concern for the integrity of local cultures and practices. A trading zone (Galison
1997) is an area in which scientific and/or technical practices can fruitfully
interact via these simplified languages or pidgins, without requiring full
assimilation. Trading zones can develop at the contact points of specialties,
around the transfer of valuable goods from one to another. In trading zones,
collaborations can be successful even if the cultures and practices that are
brought together do not agree on problems or definitions.
The trading zone concept is flexible, perhaps overly so. We might look
at almost any communication as taking place in a trading zone and demanding some pidgin-like language. For example, Richard Feynman’s diagrams
of particle interactions, which later became known as Feynman diagrams,
were successful in part because they were simple and could be interpreted
in various ways (Kaiser 2005). They were widely spread during the 1950s
by visiting postdoctoral fellows and researchers. But different schools,
working with different theoretical frameworks, picked them up, adapted
them, and developed local styles of using them. Despite their variety, they
remained important ways of communicating among physicists, and also tools
that were productive of theoretical problems and insights. It would seem to
stretch the “trading zone” concept to say that Feynman diagrams were parts
of pidgins needed for theoretical physicists to talk to each other, yet that is
what they look like.
The Kuhnian Revolution
A different, but equally flexible, concept for understanding communication across barriers is the idea of boundary objects (Star and Griesemer 1989).
In a historical case study of interactions in Berkeley’s Museum of Vertebrate
Zoology, Susan Leigh Star and James Griesemer focus on objects, rather
than languages. The different social worlds of amateur collectors, professional
scientists, philanthropists, and administrators had very different visions of the
museum, its goals, and the important work to be done. These differences
resulted in incommensurabilities among groups. However, objects can
form bridges across boundaries, if they can serve as a focus of attention in
different social worlds, and are robust enough to maintain their identities
in those different worlds.
Standardized records were among the key boundary objects that held
together these different social worlds. Records of the specimens had different meanings for the different groups of actors, but each group could
contribute to and use those records. The practices of each group could
continue intact, but the groups interacted via record keeping. Boundary objects,
then, allow for a certain amount of coordination of actions without large
measures of translation.
The boundary object concept has been picked up and used in an enormous number of ways. Even within the article in which they introduce the
concept, Star and Griesemer present a number of different examples of boundary objects, including the zoology museum itself, the different animal species
in the museum’s scope, the state of California, and standardized records
The concept has been applied very widely in STS. To take just a few
examples: Sketches and drawings can allow engineers in different parts
of design and production processes to communicate across boundaries
(Henderson 1991). Parameterizations of climate models, the filling in of
variables to bring those models in line with the world’s weather, connect
field meteorologists and simulation modelers (Sundberg 2007). In the
early twentieth century breeds of rabbits and poultry connected fanciers to
geneticists and commercial breeders (Marie 2008).
Why are there so many different boundary objects? The number and variety
suggest that, despite some incommensurability across social boundaries, there
is considerable coordination and probably even some level of communication. For example, in multidisciplinary research a considerable amount of
communication is achieved via straightforward translation (Duncker 2001).
Researchers come to understand what their colleagues in other disciplines
know, and translate what they have to say into a language that those colleagues can understand. Simultaneously, they listen to what other people
The Kuhnian Revolution 21
have to say and read what other people write, attuned to differences in
knowledge, assumptions, and focus. Concepts like pidgins, trading zones,
and boundary objects, while they might be useful in particular situations,
may overstate difficulties in communication. Incommensurability as it is found
in many practices may not always be a very serious barrier.
The divisions of the sciences result in disunity (see Dupré 1993; Galison
and Stump 1996). A disunified science requires communication, perhaps in
trading zones or direct translation, or coordination, perhaps via boundary
objects, so that its many fibers are in fact twisted around each other. Even
while disunified, though, science hangs together and has some stability. How
it does so remains an issue that merits investigation.
Conclusion: Some Impacts
The Structure of Scientific Revolutions had an immediate impact. The word
“paradigm,” referring to a way things are done or seen, came into common
usage largely because of Kuhn. Even from the short description above it is
clear that the book represents a challenge to earlier important beliefs about
Against the views of science with which we started, The Structure of Scientific
Revolutions argues that scientific communities are importantly organized around
ideas and practices, not around ideals of behavior. And, they are organized
from the bottom up, not, as functionalism would have it, to serve an
overarching goal. Against positivism, Kuhn argued that changes in theories
are not driven by data but by changes of vision. In fact, if worldviews are
essentially theories then data is subordinate to theory, rather than the
other way around. Against falsificationism, Kuhn argued that anomalies are
typically set aside, that only during revolutions are they used as a justification to reject a theory. And against all of these he argued that on the largest
scales the history of science should not be told as a story of uninterrupted
progress, but only change.
Because Kuhn’s version of science violated almost everybody’s ideas of
the rationality and progress of science, The Structure of Scientific Revolutions
was sometimes read as claiming that science is fundamentally irrational, or
describing science as “mob rule.” In retrospect it is difficult to find much
irrationalism there, and possible to see the book as somewhat conservative
– perhaps not only intellectually conservative but politically conservative (Fuller
2000). More important, perhaps, is the widespread perception that by
examining history Kuhn firmly refuted the standard view of science.
The Kuhnian Revolution
Whether or not that is true, Kuhn started people thinking about science in
very different terms. The success of the book created a space for thinking
about the practices of science in local terms, rather than in terms of their
contribution to progress, or their exemplification of ideals. Though few of
Kuhn’s specific ideas have survived fully intact, The Structure of Scientific
Revolutions has profoundly affected subsequent thinking in the study of
science and technology.
in the Sociology of Science
Robert Merton’s statement, “The institutional goal of science is the extension of certified knowledge” (1973: 270), is the supporting idea behind his
thinking on science. His structural-functionalist view assumes that society as
a whole can be analyzed in terms of overarching institutions such as religion,
government, and science. Each institution, when working well, serves a necessary function, contributing to the stability and flourishing of society. To
work well, these institutions must have the appropriate structure. Merton
treats science, therefore, as a roughly unified and singular institution, the
function of which is to provide certified knowledge. The work of the sociologist is primarily to study how its social structure does and does not support its function. Merton is the most prominent of functionalist sociologists
of science, and so his work is the main focus of this chapter, to the neglect
of such sociologists as Joseph Ben-David (1991) and John Ziman (1984),
and sociologically minded philosophers like David Hull (1988).
The key to Merton’s theory of the social structure of science lies in the
ethos of science, the norms of behavior that guide appropriate scientific
practice. Merton’s norms are institutional imperatives, in that rewards are
given to community members who follow them, and sanctions are applied
to those who violate them. Most important in this ethos are the four
norms first described in 1942: universalism, communism, disinterestedness,
and organized skepticism.
Universalism requires that the criteria used to evaluate a claim not
depend upon the identity of the person making the claim: “race, nationality, religion, class, and personal qualities are irrelevant” (Merton 1973: 270).
This should stem from the supposed impersonality of scientific laws; they
are either true or false, regardless of their proponents and their provenance.
How does the norm of universalism apply in practice? We might look to
science’s many peer review systems. For example, most scientific journals accept
articles for publication based on evaluations by experts. And in most fields,
those experts are not told the identity of the authors whose articles they are
reviewing. Although not being told the author’s name does not guarantee
his or her anonymity – because in many fields a well-connected reviewer can
guess the identity of an author from the content of the article – it supports
universalism nonetheless, both in practice and as an ideal.
Communism states that scientific knowledge – the central product of
science – is commonly owned. Originators of ideas can claim recognition
for their creativity, but cannot dictate how or by whom those ideas are to
be used. Results should be publicized, so that they can be used as widely
as possible. This serves the ends of science, because it allows researchers
access to many more findings than they could hope to create on their own.
According to Merton, communism not only promotes the goals of science
but reflects the fact that science is a social activity, or that scientific achievements are collectively produced. Even scientific discoveries by isolated individuals arise as a result of much earlier research.
Disinterestedness is a form of integrity, demanding that scientists disengage
their interests from their actions and judgments. They are expected to report
results fully, no matter what theory those results support. Disinterestedness
should rule out fraud, such as reporting fabricated data, because fraudulent
behavior typically represents the intrusion of interests. And indeed, Merton
believes that fraud is rare in science.
Organized skepticism is the tendency for the community to disbelieve new
ideas until they have been well established. Organized skepticism operates
at two levels. New claims are often greeted by arrays of public challenges. For
example, even an audience favorably disposed to its claims may fiercely question a presentation at a conference. In addition, scientists may privately reserve
judgment on new claims, employing an internalized version of the norm.
In addition to these “moral” norms there are “cognitive” norms concerning
rules of evidence, the structure of theories, and so on. Because Merton drew
a firm distinction between social and technical domains, cognitive norms are
not a matter for his sociology of science to investigate. In general, Merton’s
sociology does not make substantial claims about the intellectual content
Institutional norms work in combination with rewards and sanctions, in
contexts in which community members are socialized to respond to those
rewards and sanctions. Rewards in the scientific community are almost
entirely honorific. As Merton identifies them, the highest rewards come via
eponymy: Darwinian biology, the Copernican system, Planck’s constant, and
Halley’s comet all recognize enormous achievements. Other forms of honorific
Questioning Functionalism 25
reward are prizes and historical recognition; the most ordinary form of
scientific reward is citation of one’s work by others, seen as an indication
of influence. Sanctions are similarly applied in terms of recognition, as the
reputations of scientists who display deviant behavior suffer.
In the 1970s, the Mertonian picture of the ethos of science came under
attack, on a variety of instructive grounds. Although there were many criticisms, probably the three most important questions asked were: (1) Is the
actual conduct of science governed by Mertonian norms? To be effective,
norms of behavior must become part of the culture and institutions of
science. In addition, there must be sanctions that can be applied when
scientists deviate from the norms; but there is little evidence of strong
sanctions for violation of these norms. (2) Are these norms too flexible or
vague to perform any analytic or scientific work? (3) Does it make sense to
talk of an institutional or overarching goal of something as complex, divided,
and evolving as science? These and other questions created a serious
challenge to that view, a challenge that helped to push STS toward more
local, action-oriented views.
Ethos and Ethics
Social norms establish not only an ethos of science but an ethics of science.
Violations of norms are, importantly, ethical lapses. This aspect of Merton’s
picture has given rise to some interesting attempts to understand and define
scientific misconduct, a topic of increasing public interest (Guston 1999a).
On the structural-functionalist view, the public nature of science should
mean that deviant behavior is rare. At the same time, deviance is to be expected,
as a result of conflicts among norms. In particular, science’s reward system
is the payment for contributions to communally owned results. However,
the pressures of recognition can often create pressures to violate other norms.
A disinterested attitude toward one’s own data, for example, may go out
the window when recognition is importantly at stake, and this may create
pressure to fudge results. Fraud and other forms of scientific misconduct
occur because of the structures that advance knowledge, not despite them.
Questions of misconduct often run into a problem of differentiating between fraud and error, both of which can stand in the way of progress. The
structural-functionalist view explains why fraud is reprehensible, while error is
merely undesirable. The difference between them is the difference between
the violation of social and cognitive norms (Zuckerman 1977, 1984).
Such models continue to shape discussions of scientific misconduct. The
US National Academy of Sciences’ primer on research ethics, On Being a
Box 3.1 Is fraud common?
There are enormous pressures on scientists to perform, and to establish
careers. Yet there are difficulties in replicating experiments, there is an elite
system that allows some researchers to be relatively immune from scrutiny,
and there is an unwillingness of the scientific community to level accusations of outright fraud (Broad and Wade 1982). It is difficult, then, to know
just how common fraud is, but there is reason to suspect that it might be
Because of its substantial role in funding scientific research, the US Congress has on several occasions held hearings to address fraud. Prominently,
Congressman Albert Gore, Jr. held hearings in 1981 in response to a rash
of allegations of fraud at prominent institutions, and Congressman John
Dingell held a series of hearings, starting in 1988, that featured “the
Baltimore case” (Kevles 1998).
David Baltimore was a Nobel Prize-winning biologist who became entangled in accusations against one of his co-authors on a 1986 publication.
The events became “the Baltimore case” because he was the most prominent of the scientific actors, and because he persistently and sometimes
pugnaciously defended the accused researcher, Thereza Imanishi-Kari. In 1985,
Imanishi-Kari was an immunologist at the Massachusetts Institute of
Technology (MIT), under pressure to publish enough research to merit
tenure. She collaborated with Baltimore and four other researchers on an
experiment on DNA rearrangement, the results of which were published.
A postdoctoral researcher in Imanishi-Kari’s laboratory, Margot O’Toole, was
assigned some follow-up research, but was unable to repeat the original
results. O’Toole became convinced that the published data was not the same
as the data contained in the laboratory notebooks.
After a falling-out between Imanishi-Kari and O’Toole and a graduate
student, Charles Maplethorpe, questions about fraud started working their
way up through MIT. Settled in Imanishi-Kari’s favor at the university,
Maplethorpe alerted National Institutes of Health scientists Ned Feder and
Walter W. Stewart to the controversy. Because of an earlier case, Feder
and Stewart had become magnets for, and were on their way to becoming advocates of, the investigation of scientific fraud. They brought the case
to the attention of Congressman Dingell.
In the US Congress the case became a much larger confrontation.
Baltimore defended Imanishi-Kari and attacked the inquiry as a witch-hunt;
a number of his scientific colleagues thought his tack unwise, because of
Questioning Functionalism 27
the publicity he generated, and because he was increasingly seen as an
interested party. Dingell found in Baltimore an opponent who was important enough to be worth taking down, and in O’Toole a convincing witness.
Over the course of the hearings, Baltimore’s conduct was made to look
unprofessional, to the extent that he resigned his position as President of
However, Imanishi-Kari was later exonerated, and Baltimore was seen as
having taken a courageous stand (Kevles 1998). This raises questions about
the nature of any accusation of fraud. At the same time, though, the case
reinforces suspicions about the possible commonness of scientific fraud:
the pressure to publish was substantial; the experiments were difficult to
repeat; whether there had been fraud, or even substantial error, was open
to interpretation; and the local scientific investigation was quick, though
perhaps correct, to find no evidence of fraud.
Scientist (1995), is a widely circulated booklet containing discussions of different scenarios and principles. Ethical norms, more concrete and nuanced
than Merton’s, are presented as being in the service of the advancement of
knowledge. That is, the resolution of most ethical problems in science typically turns on understanding how to best maintain the scientific enterprise.
Functionalism about science, then, can translate more or less directly into
Is the Conduct of Science Governed by
Are the norms of science constant through history and across science? A
cursory look at different broad periods suggests that they are not constant,
and consideration of different roles that scientists can play shows that norms
can be interpreted differently by different actors (e.g. Zabusky and Barley
1997). Are they distinctive to science? Universalism, disinterestedness, and
organized skepticism are at some level professed norms for many activities in
many societies, and may not be statistically more common in science than
elsewhere. Disinterestedness, for example, is a version of a norm of rationality, in that it privileges rationality over special interests, but rationality is
professed nearly everywhere. People inside and outside of science claim that
they generally act rationally. What evidence could show us that science is
What about social versus cognitive norms? As we saw in the last chapter,
Kuhn describes the work of normal science as governed by a paradigm, and
thus by ideas specific to particular areas of research and times. If this is right,
then normal science is shaped by solidarities built around key ideas, not around
general behaviors. For example, Kuhn sees scientific education as authoritarian, militating against skepticism in favor of commonly held general
beliefs. It seems likely that cognitive norms are more important to scientists’ work than are any general moral norms (Barnes and Dolby 1970).
This point can be seen in another criticism of Mertonian norms, put
forward by Michael Mulkay (1969), using the example of the furor over
the work of Immanuel Velikovsky. In his 1950 book Worlds in Collision
Velikovsky argued that historical catastrophes, recorded in the Bible and
elsewhere, were the result of a near-collision between Earth and a planetsized object that broke off of Jupiter. The majority of mainstream scientists
saw this as sensational pseudo-science. Mulkay uses the case to show one
form of deviance from Mertonian norms in science:
In February, 1950, severe criticisms of Velikovsky’s work were published in
Science News Letter by experts in the fields of astronomy, geology, archaeology, anthropology, and oriental studies. None of these critics had at that time
seen Worlds in Collision, which was only just going into press. Those denunciations were founded upon popularized versions published, for example, in
Harper’s, Reader’s Digest and Collier’s. The author of one of these articles,
the astronomer Harlow Shapley, had earlier refused to read the manuscript
of Velikovsky’s book because Velikovsky’s “sensational claims” violated the
laws of mechanics. Clearly the “laws of mechanics” here operate as norms,
departure from which cannot be tolerated. As a consequence of Velikovsky’s
non-conformity to these norms Shapley and others felt justified in abrogating
the rules of universalism and organized skepticism. They judged the man instead
of his work . . . (Mulkay 1969: 32–33)
Scientists violated Mertonian norms in the name of a higher one: claims should
be consistent with well-established truths. One could argue that, even on
Mertonian terms, violation of the norms in the name of truth makes sense,
since those norms are supposed to represent a social structure that aids the
discovery of truths. Nonetheless, this type of case shows one way in which
moral norms are subservient to cognitive norms.
So far, we have seen that Merton’s social norms may not be as important
as cognitive norms to understanding the practice of science. But what if we
looked at the practice of science and discovered that the opposites of those
norms – secrecy, particularism, interestedness, and credulity – were common?
Questioning Functionalism 29
Do scientific communities and their institutions sanction researchers who
are, say, secretive about their work? There are, after all, obvious reasons to
be secretive. If other researchers learn about one’s ideas, methods, or results,
they may be in a position to use that information to take the next steps in
a program of research, and receive full credit for whatever comes of those
steps. Given that science is highly competitive, and given that an increasing amount of science is linked to applications on which there are possible
financial stakes (Chapter 17), there are strong incentives to follow through
on a research program before letting other researchers know about it. On
the structural-functionalist picture, norms exist to counteract local interests
such as recognition and monetary gain, so that the larger goal – the growth
of knowledge – is served. If Merton is right, we should expect to see violations of norms subject to sanctions.
In a study of scientists working on the Apollo moon project, Ian Mitroff
(1974) shows not only that scientists do not apply sanctions, but that
they often respect what he calls counter-norms, which are rough opposites
of Mertonian ones. Scientists interviewed by Mitroff voiced approval of, for
example, interested behavior (1974: 588): “Commitment, even extreme
commitment such as bias, has a role to play in science and can serve science
well.” “Without commitment one wouldn’t have the energy, the drive to
press forward against extremely difficult odds.” “The [emotionally] disinterested scientist is a myth. Even if there were such a being, he probably
wouldn’t be worth much as a scientist.” Mitroff ’s subjects identified
positive value in opposites to each of Merton’s norms: Scientific claims are
judged in terms of who makes them. Secrecy is valued because it allows
scientists to follow through on research programs without worrying about
other people doing the same work. Dogmatism allows people to build on
others’ results without worrying about foundations.
If there are both norms and counter-norms, then the analytical framework of norms does no work. A framework of norms and counter-norms
can justify anything, which means that it does not help to understand
anything. Moreover, this is not just a methodological problem for theorists,
but is also a problem for norm-based actions. When scientists act, norms
and counter-norms can give them no guidance and cannot cause them to
do anything. The reasons for or causes of actions must lie elsewhere.
Interpretations of Norms
Norms have to be interpreted. This represents a problem for the analyst,
but also shows that the force of norms is limited. Let us return to Mulkay’s
Box 3.2 Wittgenstein on rules
Wittgenstein’s discussion of rules and following rules has been seen as foundational to STS. Although it is complex, the central point can be seen in a
short passage. Wittgenstein asks us to imagine a student who has been taught
basic arithmetic. We ask this student to write down a series of numbers starting with zero, adding two each time (0, 2, 4, 6, 8, . . . ).
Now we get the pupil to continue . . . beyond 1000 – and he writes 1000, 1004,
1008, 1012. We say to him: “Look what you’ve done!” – He doesn’t understand.
We say: “You were meant to add two: look how you began the series!” – He
answers: “Yes, isn’t it right? I thought that was how I was meant to do it.” –
Or suppose he pointed to the series and said: “But I went on in the same way.”
– It would now be no use to say: “But can’t you see . . . ?” – and repeat
the old examples and explanations. – In such a case we might say, perhaps:
It comes natural to this person to understand our order with our explanations
as we should understand the order: “Add 2 up to 1000, 4 up to 2000, 6 up to
3000, and so on.” (Wittgenstein 1958: Paragraph 185)
Of course this student can be corrected, and can be taught to apply the
rule as we would – there is coercion built into such education – but there
is always the possibility of future differences of opinion as to the meaning
of the rule. In fact, Wittgenstein says, “no course of action could be determined by a rule, because every course of action can be made out to accord
with a rule” (Paragraph 201). Rules do not contain the rules for the scope
of their own applicability.
Wittgenstein’s problem is an extension of Hume’s problem of induction.
A finite number of examples, with a finite amount of explanation, cannot
constrain the next unexamined case. The problem of rule following
becomes a usefully different problem because it is in the context of actions,
and not just observations.
There are competing interpretations of Wittgenstein’s writing on this
problem. Some take him as posing a skeptical problem and giving a skeptical solution: people come to agreement about the meaning of rules because
of prior socialization, and continuing social pressure (Kripke 1982). Others
take him as giving an anti-skeptical solution after showing the absurdity of
the skeptical position: hence we need to understand rules not as formulas
standing apart from their application, but as constituted by their application (Baker and Hacker 1984). Exactly the same debate has arisen within
STS (Bloor 1992; Lynch 1992a, 1992b; Kusch 2004). For our purposes here it
is not crucial which of these positions is right, either about the interpretation
of Wittgenstein or about rules, because both sides agree that expressions
of rules do not determine their applications.
Questioning Functionalism 31
example of the Velikovsky case. The example was originally used to show
that scientists violated norms when a higher norm was at stake. Mulkay later
noticed that, depending on which parts of the context one attends to, the
norms can be interpreted as having been violated or not.
It could be argued that the kind of qualitative, documentary evidence used
by Velikovsky had been shown time and time again to be totally unreliable as
a basis for impersonal scientific analysis and that to treat this kind of pseudoscience seriously was to put the whole scientific enterprise in jeopardy. In this
way scientists could argue that their response to Velikovsky was an expression
of organized skepticism and an attempt to safeguard universalistic criteria of
scientific adequacy. (Mulkay 1980: 112)
The problem points to a more general problem about following rules
(Box 3.2): Behaviors can be interpreted as following or not following the
norms. We can explain almost any scientific episode as one of adherence
to Mertonian norms, or as one of the violation of those norms. Thus, in a
further way, they are analytically weak.
As in the case of counter-norms, the problem is not just a methodological
one. If we as onlookers can interpret the actions of scientists as either in
conformity to or in violation of the norms, so can the participants themselves. But that simply means that the norms do not constrain scientists.
By creatively selecting contexts, any scientist can use the norms to justify
almost any action. And if norms do not represent constraints, then they do
no scientific work.
Norms as Resources
Recognizing that norms can be interpreted flexibly suggests that we
study not how norms work, but how they are used. That is, in the course
of explaining and criticizing actions, scientists invoke norms – such as
the Mertonian norms, but in principle an indefinite number of others. For
example, because of his refusal to accept the truth of quantum mechanics,
Albert Einstein is often seen as becoming conservative as he grew older;
being “conservative” clearly violates the norm of disinterestedness (Kaiser
1994). Einstein is so labeled in order to understand how the same person
who revolutionized accepted notions of space and time could later reject
a theory because it challenged accepted notions of causality: otherwise
how could the twentieth century’s epitome of the scientific genius make
such a mistake? Implicit in the charge, however, is an assumption that
Einstein was wrong to reject quantum mechanics, an assumption that
quantum mechanics is obviously right, whatever the difficulties that some
people have with it. Werner Heisenberg, one of the participants in the debate,
discounted Einstein’s positions by claiming that they were produced by closedminded dogmatism and old age. If we believe Heisenberg, we can safely
ignore critics of quantum mechanics. How are norms serving as resources in
this case? They are being used to help eliminate conflicting views: because
Einstein’s opposition to quantum mechanics violated norms of conduct, we
do not have to pay much attention to his arguments.
Whether a theory stands or falls depends upon the strengths of the arguments put forward for and against it (it also depends upon the theory’s
usefulness, upon the strengths of the alternatives, and so on). However,
it is rarely simple to evaluate important and real theories, and so complex
arguments are crucial to science and to scientific beliefs. Norms of behavior can play a role, if they are used to diminish the importance of some
arguments and increase the importance of others. Supporters of quantum
mechanics are apt to see Einstein as a conservative in his later years.
Opponents of quantum mechanics are apt to see him as maintaining a youthful skepticism throughout his life (Fine 1986). Norms are ideals, and like
all ideals, they do not apply straightforwardly to concrete cases. People with
different interests and different perspectives will apply norms differently.
We are led, then, from seeing norms as constraining actions to seeing them
as rhetorical resources. This is one of many parallel shifts of focus in STS, of
which we will see more in later chapters. For the most part, these are changes
from more structure-centered perspectives to more agent- or action-centered
perspectives. This is not to say that there is one simple theoretical maneuver that characterizes STS, but that the field has found some shifts from
structure- to action-centered perspectives to be particularly valuable.
The study of “boundary work” is one approach to seeing norms as
resources (Gieryn 1999). When issues of epistemic authority, the authority
to make respected claims, arise, people attempt to draw boundaries. To have
authority on any contentious issue requires that at least some other people
do not have it. The study of boundary work is a localized, historical, or antifoundational approach to understanding authority (Gieryn 1999). For
example, some people might argue that science gets its epistemic authority
from its rationality, its connection to nature, or its connection to technology
or policy. We can see those connections, though, as products of boundary
work: Science is rational because of successful efforts to define it in terms of
rationality; science is connected to nature because it has acquired authority
to determine what nature is; and scientists connect their work to the benefits
Questioning Functionalism 33
Box 3.3 Cyril Burt, from hero to fraud
Sir Cyril Burt (1883–1971) was one of the most eminent psychologists of the
twentieth century, and knighted for his contributions to psychology and to
public policy. Burt was known for his strong data and arguments supporting hereditarianism (nature) over environmentalism (nurture) about intelligence. After his death, opponents of hereditarianism pointed out that
his findings were curiously consistent over the years. In 1976 in The Times,
a medical journalist, Oliver Gillie, accused Burt of falsifying data, inventing
studies and even co-workers. This public accusation of fraud against one
of the discipline’s most noted figures posed a challenge to the authority of
the psychology itself (Gieryn and Figert 1986).
Early on, his supporters represented Burt as occasionally sloppy, but
insisted that there was no evidence of fraud. Burt’s work was difficult, they
argued, and it was therefore understandable that he made some mistakes.
No psychologist’s work would be immune from criticism. In addition, Burt
was an “impish” character, explaining his invention of colleagues. These
responses construed Burt’s work as scientific, but science as imperfect. That
is, psychologists drew boundaries that accepted minor flaws in science, and
thus allowed a flawed character to be one of their own.
In addition to denying or minimizing the accusations, responses by psychologists involved charging Gillie with acting inappropriately. By publishing
his accusations in a newspaper, Gillie had subjected Burt’s work to a trial not
by his peers. The public nature of the IQ controversy raised questions about
motives: Were environmentalists trumping up or blowing up the accusations to discredit the strongest piece of evidence against them? Psychologists
insisted that there be a scientific inquiry into the matter, and endorsed
the ongoing research of one of their own, Leslie Hearnshaw, who was working on a biography of Burt. That biography ended up agreeing with the
accusers. However, it rescued psychology by banishing Burt, and using the
idea that “the truth will out” in science to recover the discipline’s authority.
Hearnshaw argued that the fraud was the result of personal crisis, especially late Burt’s in life, and was the result of his acting in a particularly
unscientific manner. Most importantly, he argued that Burt was not a real
scientist, but was rather an outsider who sometimes did good scientific work:
The gifts which made Burt an effective applied psychologist . . . militated
against his scientific work. Neither by temperament nor by training was he
a scientist. He was overconfident, too much in a hurry, too eager for final results,
too ready to adjust and paper-over, to be a good scientist. His work often had
the appearance of science, but not always the substance. (Hearnshaw, quoted
in Gieryn and Figert 1986: 80)
of technology or the urgency of political action in particular situations when
they are seeking authority that depends on those connections. Yet those same
connections are made carefully, to protect the authority of science, and are
countered by boundary work aimed at protecting or expanding the authority
of engineers and politicians (Jasanoff 1987).
Boundary work is a concept with broad applicability. Norms are not
the only resources that can be used to stabilize or destabilize boundaries.
Organizations can help to further goals while maintaining the integrity
of established boundaries (Guston 1999b; Moore 1996). Boundary work
can be routine, occurring when there are no immediate conflicts on the
horizon (Kleinman and Kinchy 2003; Mellor 2003). Examples, people,
methods, and qualifications are all used in the practical and never-ending
work of charting boundaries. Textbooks, courses, and museum exhibits,
for example, can establish maps of fields simply through the topics and
examples that they represent (Gieryn 1996). In fact, little does not participate in some sort of boundary work, since every particular statement
contributes to a picture of the space of allowable statements.
The Place of Norms in Science?
The failings of Merton’s functionalist picture of science are instructive. Merton
can be seen as asking what science needs to be like, as a social activity, in
order for it to best provide certified knowledge. His four norms provide an
elegant solution to that problem, and a plausible solution in that they are
professed standards of scientific behavior.
Nonetheless, these norms do not seem to describe the behavior of scientists, unless the framework is interpreted very flexibly. But if it is interpreted
flexibly then it ceases to do real analytic or explanatory work. Going a
little deeper, critics have also challenged the idea that science is a unified
institution organized around a single goal or even a set of goals. Instead,
the sciences and individual scientific institutions are contested – by governments, corporations, publics, and scientists themselves. Does the idea of an
overarching goal, for an entity as large and diffuse as science, even make
sense? Could an overarching goal for science have any effect on the actions
of individual scientists?
As a result of these arguments, critics suggest that science is better understood as the combined product of scientists acting to pursue their own goals.
Merton’s norms, then, are ideological resources, available to scientific actors
for their own purposes. They serve, combined with formalist epistemologies,
as something like an “organizational myth” of science (Fuchs 1993).
Questioning Functionalism 35
Still, we can ask how ideologies like Merton’s norms affect science as
a whole. It may be that their repeated invocation leads to their having real
effects on the shape of scientific behavior: they are used to hold scientists
accountable, even if their use is flexible. We might expect, for example,
that the repeated demand for universalism will lead to some types of discrimination being unacceptable – shaping the ethics of science. Along with
other values, Merton’s norms may contribute to what Lorraine Daston (1995)
calls the “moral economy” of science. While science as a whole may not
have institutional goals, combined actions of individual scientists might
shape science to look as though it has goals (Hull 1988). Even though boundaries, in this case boundaries of acceptable behavior, are constructed, they
can have real effects.
An Efficient Meritocracy or an Inefficient Old
Of the 55 Nobel Laureates working in the United States in 1963, a full 34
of them had studied or collaborated with a total of 46 previous prizewinners (Zuckerman 1977). Not only that, but those who had worked with
Nobel Laureates before they themselves had done their important research
received their prizes at an average age of 44, compared with an average
age of 53 for the others. Clearly scientists tend to form elite groupings.
Are these groupings the tip of a merit-based iceberg, or are they artifacts
of systems of prestige gone awry? Is the knowledge for which elites are
recognized intrinsically and objectively valuable, or does it become so because
of its association with elites?
The renaissance philosopher Francis Bacon thought that the inductive
method he had set out for science would level the differences among intellectual abilities and allow science to be industrialized. Three hundred years
later, Spanish philosopher José Ortega y Gasset claimed that “science has
progressed thanks in great part to the work of men astoundingly mediocre,
and even less than mediocre.” Despite such pronouncements, a small number of authors publish many papers and a great number publish very few.
Roughly 10% of all scientific authors produce 50% of all scientific papers (Price
1986). Similarly, a small number of articles are cited many times and a great
number are cited very few times: an estimated 80% of citations are to 20%
of papers (Cole and Cole 1973). If the number of citations is a measure of
influence then these figures suggest that a small number of publications are
quite influential, and the vast majority make only a small contribution to
future advances. To use citations to represent influence can be misleading
(Box 4.1), but, these figures are so striking that even if number of citations