a sociological theory of scientific change

A Sociological Theory of Scientific ChangeAuthor(s): Stephan FuchsSource: Social Forces, Vol. 71, No. 4 (Jun., 1993), pp. 933-953Published by: Oxford University PressStable URL: http://www.jstor.org/stable/2580125 .Accessed: 15/06/2014 09:19

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A Sociological Theory of Scientific Change

S1EPHAN FUCHS, University of Virginia

Abstract

In current science studies, there are only few systematic efforts at explaining how different scientific specialties change over time. Such specialties are viewed here as organizations in which workers deal with various degrees of task uncertainty and mutual dependence. The sociological theory of change suggests that scientific change is generally triggered by competition, but that various types of change depend on the social organization and status of scientific groups. Some fields change through permanent discoveries, some through specialization and cumulation, yet others change through cognitivefragmentation. This argument can synthesize the various independent branches of contemporary science studies. The proposed theory has wider significance for some core problems in sociology, such as the relationship between the natural and social sciences, the prospectsfor a science of society, and the possibility of cumulative progress.

Despite a growing interest in the historical and social dynamics of science, rather than in its institutional norms or cognitive logic, there is minimal consensus in current studies of science on how and why scientific change occurs. There is, however, widespread agreement that science does not change in the ways suggested by orthodox epistemology,1 which attributes scientific change to cognitive and internal factors. According to this model, acts of discovery are private episodes of revelation and cannot be accounted for in any logic of research. In contrast lies the public validation of discovery claims, which is a rational process of following the rules of scientific method. These rules call for crucial tests to settle controversies and for replications to validate discoveries. In this way, scientific knowledge gradually approximates truth through conjectures and refutations (Popper 1969).

The philosophical model of scientific change reduces change to cumulative progress and rational selection. It is now widely discredited as unrealistic. To a large part, this is due to Kuhn's The Structure of Scientific Revoluitions ([1962] 1970). Kuhn's theory, however, retains strongly realist overtones and is empirically questionable.2 In fact, his insistence on the dogmatic traditionalism

*I would like to thank Randall Collins, Bryan Pfaffenberger, and the anonymous reviewersfor comments on an earlier draft. Direct correspondence to Stephan Fuchs, Department of Sociology, University of Virginia, Charlestville, VA 22903.

? The University of North Carolina Press Social Forces, June 1993, 71(4):933-953


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934 / Social Forces 71:4, June 1993

of normal science makes the very occurrence of major paradigmatic breakthroughs somewhat mysterious. In arguing that revolutions are triggered by the accumulation of anomalies, Kuhn's explanation of change remains philosophical. He does not account for the many possible reactions to perceived anomalies. One can ignore them, explain them away, try to accommodate them into established knowledge, or make minor modifications in a trivial part of the theory (Hesse 1980; Quine 1953). Alternatively, mavericks may celebrate anomalies as welcome challenges to the cognitive and social establishment. Generally speaking, how groups - be they groups of scientists or other people

respond to the Strange, the Foreign, the Other, or Kuhnian empirical anomalies appears to depend on their social structure (Bloor 1982,1983; Douglas 1966, 1970; Rudwick 1982).

An even more serious problem is that Kuhn's theory expects only two basic types of scientific activity: normal and revolutionary science. Revolutions, however, are as rare in science as in other areas of society. Most scientific change appears to be nonrevolutionary. Holton (1988), in his study of relativity theory, has suggested that revolutions are often returns to classical ideals of simplicity and uniformity. Sociological studies suggest that even the few revolutions that do occur are dramatic culminations at the end of a long series of smaller incremental changes, not sudden and holistic gestalt shifts (Mullins 1975).

It seems that the major failure underlying these various problems with Kuhn's theory is the failure to allow for more variation in scientific practice. The normal/revolutionary dichotomy is not complex enough to account for the many ways in which science may change (Mulkay 1975). I shall argue that various types of scientific change occur, in various areas of science, at the same time and do not, as Kuhn suggests, alternate through time.

The Theory of Scientific Organizations

The sociological theory of scientific change to be presented here is part of the theory of scientific organizations (TSO) (Fuchs 1992). This theory views scientific specialties as reputational work organizations in which material resources and social structures shape how scientists perform their work. A specialty is a group of practitioners with similar training, attending the same conferences, reading and citing the same bodies of literature, and being more likely to talk to each other than to members of different specialties, considerable overlaps and mobility notwithstanding.3 Specialties are usually sharply stratified (Mullins et al. 1977). They are made up of a small core of highly productive and visible people, a semiperiphery of solid but ordinary producers with much less visibility, and a large periphery of rather inactive transients (Price 1986).

The theory of scientific organizations is rooted in three broad sociological traditions: Durkheimian and Neo-Durkheimian sociology of knowledge, the technological tradition in organizational research, and the materialist theory of the mind.4 The core of the Durkheimian and Neo-Durkheimian argument is that styles of thinking and modes of perceiving the world are shaped by social structure. Knowledge is social imagery in that it reflects the parameters of social



Theory of Scientific Change / 935

organization. One such parameter is the level of integration in the group - what Durkheim calls "solidarity," Douglas calls "group," the organizational literature refers to as "coupling," and Collins calls "ritual density." Generally, the more cohesive and homogeneous a group, the greater the pressures for rigid conformity to its cognitive standards and strict demarcation from other groups. Such groups produce restricted and localistic cognitive styles with reified symbols and dogmatic rules. Conversely, loosely coupled and heterogeneous groups will develop more cosmopolitan and liberal cognitive styles (Fuchs & Case 1989, 1993).

The technological school in organizational research, the second root of the theory of scientific organizations (TSO), argues that social structures and cognitions are formed by the ways in which groups or organizations perform their work, or their "technology." When the work is certain, routine, and predictable, the social structures of coordination and control will resemble a Weberian bureaucracy with its formal rules, codified procedures, and administrative hierarchies. The corresponding cognitive mode emphasizes conceptual orderliness and procedural correctness. Conversely, when the tasks are uncertain, nonroutine, and variable, the social structure will be more informal, flexible, and decentralized. The cognitive style in such "organic" organizations will tolerate more discretion and innovativeness.

The third root of TSO is the materialist theory of consciousness. In its classical formulation by Marx and Engels ([1846] 1970), the theory argues that how people think is related to how the material means of mental production are distributed. Those who control these means are in a good position to control how ideas are produced as well, and even these ideas themselves. Science is not exempt from this; it is not the pure domain of a disinterested and free-floating stratum of intellectuals. Rather, scientific work, especially the Big Science of modern societies, requires a great deal of hardware and organizational facilities, and so is constrained by the conflict-laden and stratified distribution and control of various kinds of property. Furthermore, intellectual strategies and agendas are firmly embedded in this structure, and are not as innocent and disinterested as they like to present themselves.

TSO has two more immediate predecessors: Randall Collins's (1975) theory of the intellectual world, and Whitley's (1984) comparative typology of scientific fields. Collins argues that the structures of scientific specialties are determined by the severity of "coordination problems" and the degree of "uncertainty." Coordination problems between scientists are aggravated by exogenous variables such as the "need for group validation" or the number of scientists (positive relationships). The degree of uncertainty depends on variables like the existence of a workable paradigm (negative relationship) and the amount of subjective creativity (positive relationship). Cross-classifying coordination problems and uncertainty, Collins ends up with four typical structures for the organization of scientific specialties. These are very similar to Perrow's (1967) and Stinchcombe's (1959) classic typologies. For example, a combination of high uncertainty and severe coordination problems produces a "collegiate profes- sion," while low uncertainty combined with mild coordination problems yields a "regular bureaucracy." The assumption is that work and cognitive styles differ in these various structures, but that assumption is not systemnatically developed.




Like Collins, Whitley (1984) assumes that organizational structures in science are due to variations in two basic parameters: the amount of task uncertainty faced in scientific work, and the degree of "mutual dependence" between scientists. Both variables are affected, in somewhat ambiguous ways, by "contextual factors" such as the degree to which scientific groups are autonomous in allocating reputation to workers, the degree of concentration in resources, and the diversity of audiences for scientific results. For example, high reputational autonomy and high resource concentration would increase mutual dependence, since scientists must then turn to each other and to those who control the resources in order to do their work and receive recognition for it. The same contextual conditions would decrease task uncertainty, however, for powerful reputational and resource-controlling elites would set rather uniform and widely observed standards for scientific work.

The core of Whitley's discussion is a conceptual schema of seven typical organizational structures in science that are due to various values for uncertainty, dependence, and the contextual variables. For example, there are "conceptua- lly integrated bureaucracies" (Collins's "regular bureaucracies"), such as physics, with very high reputational autonomy and extremely concentrated resources. Workers in such organizations depend very closely on the elites who control reputations and resources. Task uncertainty is generally low because the elites are too powerful for alternative modes of work and deviant styles of interpretation to challenge the orthodoxy. As a result, work in physics is rather routinized, well structured, and theoretically integrated.5 At the other extreme of the spectrum, there are "fragmented adhocracies," such as contemporary sociology. In such organizations, mutual dependence is low while task uncertainty is very high because reputations may be gained from a variety of audiences, and resources are widely dispersed. The style of work differs greatly from that in bureaucracies: it is not as well structured, more idiosyncratic, and rather localistic.

Collins and Whitley lay the groundwork for an organizational theory of science. Collins, however, does not clearly distinguish between cognitive and social-structural variables, and Whitley is too concerned with conceptual typologies and analytical definitions that tend to obscure the causal texture of his argument. Neither author establishes clear relationships between technologies, structures, and the contents of scientific knowledges. Moreover, they are more concerned with the comparative statics of science than the dynamics of scientific change.6 That is, they do not show how various types of change covary with the organizational conditions of scientific work.

From Collins and Whitley, however, I take the idea that mutual dependence and task uncertainty are two central organizational variables explaining how and why various types of change occur in various areas of the sciences. It is important from the outset to emphasize that organizational structures and forms of change vary. There is a strong tendency in studies of science not to allow for enough variation, especially in the constructivist sociology of scientific knowledge (SSK) which has almost exclusively produced interpretive and ethno- graphic case studies of individual events and sites in science. This pronounced microfocus has largely driven out comparative studies of the larger structural and organizational settings of scientific work. Not all science is the same,




however, and it is as misleading to speak of the "contextual" and "local" nature of science7 as it is to portray all science as rational and driven by some universal logic, as did the conventional philosophers. Instead, it is crucial to account for variations in scientific practice and to construct theories that can explain such variations. Since Mill we know that where nothing is allowed to vary, nothing can be explained.

Competition and Change

Science is an intensely competitive and antagonistic field (Bourdieu 1991), and this very competition seems to be an important force driving scientific change (Hagstrom 1965; Holton 1988; R. Collins 1989). Competition varies along two dimensions. First, it depends on the kinds of resources that are primarily at stake. Thus, it arises over material resources such as grants, jobs, and access to research facilities, but it also arises over more symbolic resources, such as reputation, journal space, or innovations.8 Material and symbolic resources are closely linked: without material resources, one cannot contribute to science; without such contributions, researchers would have difficulties getting funded. Material and symbolic capital do not only accumulate over time; they are also convertible, to some degree, into each other and then speed up the rates at which they can be increased.

Competition may also vary in intensity depending on the size of the stakes, and how close the struggle is to a zero-sum game. Hence, competition is most intense when people compete for valuable resources, and when someone's gains are someone else's losses. For example, competition for a discovery that may lead to the Nobel Prize is more intense than competition for an intramural research grant, or for general visibility that does not lessen someone else's recognition.

Considering these important distinctions in the analysis to follow, we can probably simplify now and say that scientists compete for recognition and reputation which open up access to the means of intellectual production. These means, in turn, are necessary to conduct more work that may gain yet more recognition. Since scientists must recognize and use one another's work, they compete for their peers' attention. Because audiences do not want to listen repeatedly to statements that are old and familiar, scientists must produce statements that are, in some ways and to some extent, new. The highest rewards go to the scientists who are seen to advance the state of knowledge. This is the critical connection between competition and change in science: competition drives change because it forces people to say something new.

But competition and change are variables. Hence, the pressure to say something new is not equally strong in all areas of science. Likewise, that which constitutes newness as well as the types of changes that are triggered by competition are not the same everywhere. My hypothesis is that the types of change depend on the organizational variables identified by Collins and Whitley; that is, on the level of mutual dependence between scientists, and the




amount of task uncertainty encountered in daily work. Of course, these two variables are not genuinely exogenous variables and are treated as such here only for reasons of presentational economy.9

Task Uncertainty and Mutual Dependence

The organizational literature uses the concept "task uncertainty" in at least three different, but often confounded, ways. First, it is used to describe the objective properties of work technologies and raw materials in organizations (e.g., Perrow 1967; Shrum & Morris 1990; Stinchcombe 1990; Whitley 1984). Structural uncertainty is high when many exceptions and unexpected situations are encountered during work, when there are no clear procedures and rules for doing the work, and when the raw materials - be they things, people, or symbols - and the technologies are complex and changing.

The second and third meanings of "task uncertainty" refer to individual and collective perceptions or constructions of the work process. Perceived uncertainty is high when information is incomplete and inadequate, when it is difficult to anticipate outcomes, and when the interpretation and evaluation of results are controversial and ambiguous. It is usually assumed that structural and perceived uncertainty are closely associated (Lawrence & Lorsch [1967] 1986). However, this is not the case for the third meaning, where "task uncertainty" denotes an ideological strategy. The claim that work is uncertain is then followed by claims that only professionals with training and credentials can handle the tasks, while lay people are not qualified to control or even assess professional work. Such "constructed" uncertainty is an ideological move for professional autonomy or even monopoly (Crozier 1964; Larson 1977).

In the present context, the first two meanings of "task uncertainty" are crucial. That is, uncertainty is viewed here as a property of certain tasks, and of perceptions of these tasks. Uncertainty is high when the work is nonroutine and unpredictable; it is low when the work is routine, repetitive, and predictable. In addition, there are several sources of uncertainty, where sources are the things people compete for. As Stinchcombe (1990) notes, organizations and organizational sectors face various kinds of uncertainty, and their structures differ depending on what kind of uncertainty they are confronted with. Scientists may be uncertain about their jobs, about their funding sources, about what other people are doing and whether they are doing it faster than them, about who will win the Nobel Prize and for what discovery, or about what world ethnomethodologists live in. Different uncertainties lead to various reactions and modes of coping, and we must remember this when investigating the effects of competition on various types of change.

"Mutual dependence" indicates the level of social integration in a group or network. It covers much the same dimensions as Douglas's "group," Collins's "ritual density," Durkheim's "solidarity," or Perrow's "coupling." Since scientists can gain reputations and resources only when other scientists recognize their work, they depend on each other, and especially on the gatekeepers who regulate access to critical means of production, such as lab space and time, journal space, grants, or citations. Mutual dependence is high



Theoxy of Scientific Change / 939

when the means of production are very concentrated, and when the contacts between scientists take place in dense networks. In this situation, scientists must closely coordinate their work with that of others and focus their efforts on shared topics (Whitley 1984). This is a "high group" network with a well- defined collective identity, clear boundaries, and a confident belief in shared practices.

Conversely, low mutual dependence occurs when the means of scientific production are more decentralized, and when the communication networks are only loosely coupled. Under these conditions, various "schools" and "perspectives" coexist rather independently, and the amount of individual discretion over work is very high.,There are only weak collective controls over the production of knowledge, and the whole system's cultural outlook is rather cosmopolitan and pluralistic.

The argument, then, is that competition leads to scientific change, but that this effect depends on task uncertainty and mutual dependence. Depending on these latter variables, competition may lead to three basic types of change: permanent discovery, specialization and cumulation, and fragmentation. In addition, there is stagnation, with which I will deal only in passing.10 Figure 1 presents this argument.

Permanent Discovery

Specialty and scientometric studies have consistently stressed the importance of "research fronts" or "invisible colleges" for sustained growth in mature sciences (Crane 1972; Price 1986). These are the small and closely coupled core groups in a specialty who work on highly innovative and central problems, and often define where the larger field as a whole is moving. These groups are very elitist and exclusive, for they consist of the most productive, most frequently cited, most widely recognized, and most highly rewarded practitioners in a specialty (Cole & Cole 1973). There is a great deal of face-to-face interaction, personal acquaintanceships, and preprint exchanges between members of invisible colleges; creating a strong sense of belonging to the same group. Often, new members are recruited through master-apprentice ties (R. Collins 1989; Zuckerman 1977).

Forming the apex of the stratification system in science, invisible colleges are typically concentrated at a few very prestigious research sites and lab- oratories (Small 1977). These are linked through national and, sometimes, international networks to other very prestigious sites and groups (Zuckerman 1977). The cosmopolitan intellectual leaders operate as powerful trend setters and gatekeepers who control access to the symbolic and material means of scientific work (Hagstrom 1965). The leaders are expert members of funding agency boards, research directors regulating access to the lab equipment, and editors of or referees for the most prestigious journals in a specialty. As Traweek (1988) shows for high-energy physics, such control is the more far- reaching the more expensive and concentrated the resources for scientific production are. The leaders occupy central positions in the relevant social networks and mediate indirect links between other researchers. In Goffmanian




FIGURE 1: Three Types of Scientific Change

[r Task Uncertainty

Low IHigh

Low Stagnation Fragmentation Mutual

Dependence I High Specialization/ Permanent Discovery Cumulation

terms, these are the front-stage performers who are at the centers of attention and speak and act on behalf of the organization. They are order givers; in the sense of deciding what the "hot" topics are, not in the sense of bureaucratic domination. They are also the scientists who are most often engaged, by forming small and exclusive "core sets," in closing important scientific controversies and deciding on their outcomes (H. Collins 1981; Pinch 1981, 1986). Members of the core group also form the "third parties" that settle priority conflicts over multiple discoveries (Cozzens 1989).

Due to very high stakes, research fronts are fiercely competitive. The most dramatic form of competition is competition over priority and property rights to major scientific breakthroughs. The major source of uncertainty is how the world will be described in the future, and who will have the opportunity to describe it. Since competition is fierce, knowledge is changing and advancing very rapidly. The half-life of research papers is extremely short, meaning that most citations are to very recent contributions (Hargens 1991; Hargens & Felmlee 1984; Mullins 1975). This rapid obsolescence of past discoveries generates intense pressures to constantly update one's knowledge of the field and keep in touch with the most recent developments. Scientists cannot gain this sort of knowledge from the published sources, but only from membership in the informal communication networks. This is easier for those already connected to the core groups, such as students or collaborators of eminent scientists who have a head start in making their own discoveries.

Structurally, this situation is similar to organizations with sophisticated and vulnerable technosystems that are closely coupled and reciprocally linked, such as nuclear power plants. Perrow (1984) shows that disturbances in such organizations ramify quickly and unpredictably throughout the entire system, regularly and inevitably triggering disastrous "normal accidents." Such accidents are not due to operator failure or faulty designs, but are built into the very structure of the organization and, as such, cannot be prevented. Incidents in one part of the system will trigger other incidents in related parts, and so incidents and their feedback effects on other incidents accumulate quickly and unpredictably into major systemic accidents.




Research fronts are structured in just this dense and highly interactive way. Cohen (1985) summarizes his studies of revolutionary scientific change remarking that change is "inevitable" and "cannot be prevented" - the exact words Perrow uses to describe normal accidents in closely coupled systems. Griffith and Mullins (1972) have observed this correlation between close coupling and innovative change for a number of fields, including molecular biology and quantum physics. In his comparative sociological history of philosophy, Collins (n.d.) shows that the take-off of modem science in the 1600s was in part due to the increasing density of overlapping intellectual networks around a creative core. In the scientometric literature, this fact shows up as small sets of highly interactive co-citation clusters (Small & Crane 1979). High mutual dependence precisely means that what happens in one part of the network will have consequences for other parts, and vice versa. Such snowball effects are the more likely the more the scientists propagating an innovation occupy central and well connected positions in the group, and so have high visibility and command a great deal of attention to their work.

As in other walks of life, not all voices carry equally far in science. Some voices are stronger than others, and so can be heard by more people, even in the more remote parts of the structure. The more one can be heard, the more people pay attention, and the more they pay attention, the more forceful one's statements become. Often, like in the normal accidents that Perrow describes, the beginnings of major change may be quite modest and restricted to those who work together in close physical proximity. Garfinkel, Lynch, and Living- ston (1981) describe the microreality of such a situation. But to become major innovations, discoveries must also travel along the phone and mail lines, and through the informal and indirect links in the networks that connect distant people to one another. And the more closely people are linked by such lines, the more likely an innovation will spread like a normal accident.

In this process, an innovation will not remain unchanged. Other scientists will modify it according to their own agendas and interests. This is why discoveries, in their "final form," are often very different from their initial shape (Brannigan 1981; Kuhn 1977). As a result, historians often have great difficulty deciding just what was discovered by whom and exactly when.

Innovations travel as far as the networks that link people together in invisible colleges, and to other invisible colleges in related specialties and fields. The "reach" of an innovation is essentially the reach of the networks sur- rounding the innovating core groups. This follows from the fact that statements and innovations need the attention and active support of other people to become something more than mere subjective opinions. Discoveries must be actively pushed through the structure; they do not travel well alone. This process of "scientific marketing" is very similar to the process of transforming inventions into innovations analyzed by Schumpeter (1942). Essentially, mere inventions turn into successful innovations when a social structure is in place that disseminates and "sells" them. Without such a structure, inventions remain utopian projects that will not make a real difference (Stinchcombe 1990). The core groups in the network are in a good position to produce successful




innovations because their agendas are more visible throughout the specialty. Because they are well connected, they are more likely to enroll the support of other people and "translate" their statements into the core agenda.11

Hence, an invention by itself is never strong enough to trigger the effects necessary to produce major change. A discovery may perfectly well uncover a new truth about the world and advance our understanding in dramatic ways, but if no one pays attention to it, especially not the leaders and core groups, it is unlikely to generate important results. Barber (1961) discusses several historical cases in which low professional standing and peripheral status of scientists impeded the recognition of their discoveries. To paraphrase Foucault, one must already be in the truth to speak the truth.

How Are Discoveries Made?

Discoveries are not sudden acts of creative revelation by isolated geniuses who then simply convince their colleagues that they have stumbled upon a new truth. Major creativity is not just a personal talent, but a property of certain groups. One condition for doing creative work is being connected to other creative people, and so creativity travels through chains linking the eminent people in a field to other eminent people (R. Collins 1989; Zuckerman 1977). It is not impossible to do creative work on the margins of a field. In fact, it may even be conducive to creativity to spend some time away from the scientific mainstream. But too much isolation will lead to a loss of the creative energy that comes from recognition and encouragement by one's peers, and to unawareness of where the research fronts have moved in the meantime. This explains Hagstrom's (1965) finding that organizational isolates are generally not very productive, and that the most creative scientists are located at the core of multiple overlapping communication networks.

Research fronts house science-in-the-making, not ready-made science (Latour 1987). That is, scientific knowledge has not yet achieved factual and objective status, but is subject to permanent deconstructions, controversies, and revisions. At the research frontiers, work is too uncertain and innovative for formal procedures to determine how work is actually to be carried out. These may, at a later date, be used to rationalize scientific practice for journal publications, public presentations, or Whiggish historical success stories. But such rules of method are not available to prescribe the steps leading to scientific innovation. It could be said that research front science is more situational and opportunistic trial-and-error work. This is the type of science described in constructivist studies. They portray scientific work as pragmatic tinkering and informal negotiations, not Popperian rule-following (Knorr-Cetina 1981; Pickering 1984).

We have here another reason for why it is so crucial to be connected to the core groups in innovative research front science. Researchers cannot learn this type of science from textbooks and "crucial experiments," but from teachers and mentors in direct personal interaction (Zuckerman 1977). The knowledge and skills required to do innovative work are more tacit and personal, not rule-like and propositional (H. Collins 1985; Polanyi 1964). They can only be acquired during socialization and "enculturation" into a group, not from written accounts




and published demonstrations. Membership in the core makes it also easier to place sensible bets on where promising new knowledge is likely to be found in the future (Stinchcombe 1990). Researchers on the margins of a scientific field have a significantly more difficult time discerning where the "hot" centers are about to move, and thus frequently are too late to make a difference.

Working in invisible colleges means dealing with constantly changing innovative problems, or with high task uncertainty. Research on complex organizations suggests that workers tackling highly uncertain tasks must be given considerable discretion and autonomy (Crozier 1964). No rigid hier- archical system of bureaucratic supervision is in place to control expert workers. High uncertainty leads to more informal and decentralized social structures that coordinate activities through mutual consultations and ad hoc adjustments, rather than through hierarchies and formal, or methodological, rules (Shrum & Morris 1990).12 In research on organizations, this management style is known as "organic" (Burns & Stalker 1961; Woodward 1965), or as "coordination through feedback" (Thompson 1967).

At the same time, however, individual discretion is tempered by the high level of mutual dependence between scientists working in research front groups. Because the group is very small, strongly interactive, and exclusive, collegiate control over individual work is rather tight, without stifling individual creativity and initiative. Most importantly, the close social coupling of invisible colleges assures that advances in knowledge will be, to some extent, orderly and focused, instead of idiosyncratic and unstructured. In contrast, change is much more erratic and unfocused in loosely coupled fields, such as sociology or literature. In such fields, high uncertainty is combined with low mutual dependence, which results in a very individualistic or even idiosyncratic mode of intellectual work. But high social density always tempers the possibly disorganizing effects of high uncertainty and pronounced individual autonomy. In this way, novelty will be balanced by tradition. Kuhn (1977) calls this precarious balance the "essential tension."

At the research fronts, then, the most dramatic form of competition is competition over discoveries, and the most crucial source of uncertainty is how the world will be invented in the future and by whom. Since organizations tend to grow toward their major uncertainties (Stinchcome 1990), the communication lines will be full of gossip on who is doing what. Consequently, anticipations, priority conflicts, and discovery contests are most frequent and intense here (Freudenthal 1984; Gieryn 1982; Merton 1973). Research fronts are very "political." Multiple discoveries, and the ensuing priority and property conflicts, are more likely when closely coupled groups push the frontiers of knowledge in the same general direction (Gaston 1973; Hagstrom 1974). Note that multiples make Perrowian normal change accidents even more likely, for the fire is being started in more than one part of a tightly coupled structure.

Specialization and Cumulation

In the sociology of science and scientific knowledge, there is a widespread distinction between two types of science: controversial, conflictual, and uncertain science as it is actually being produced on the laboratory workbench,




versus objective, consensual, and authoritative science as it is portrayed in textbooks, public presentations, and rational historical reconstructions. Fol- lowing Kuhn, the relationship between the two is usually presented as temporal alternation. I would suggest, however, that different ways of doing science actually coexist, but are practiced by different groups at different levels in the stratification system.

The constant advances and permanent discoveries of research front science- in-the-making are the work of a small number of highly productive, prestigious, and widely recognized scientists whose contributions are frequently cited, win the highest awards, and thus define the cognitive standards of the field as a whole (Menard 1971). The vast majority of scientists, however, are not part of these elitist networks. They publish fewer articles that are rarely, if ever, cited, and work on more routine problems and more marginal areas that do not draw a great deal of attention (Price 1986). As Harry Collins (1981) remarks,

nearly all the experience of experimental work of nearly all scientists is drawn from compelling published reports or from uncontroversial settings where the quality of experimental work is not examined in a committed way. (14)

Normal scientific puzzle-solving follows more standard routines and established procedures. Task uncertainty is lower here than at the frontiers of science, and the major source of uncertainty is not priority struggles and discovery claims, but jobs, promotions, and material resources.13 Work on less uncertain tasks can more readily follow the formal guidelines for research codified by orthodox epistemology.14 Thus, it is probably to this more orderly and structured area of science that orthodox epistemology, with its ritual affirmation of methodological propriety and procedural purity, refers most directly. Bureaucracy, normal science, and epistemology emerge when people are fairly certain what to do and how to do it, while informal organization, science-in-the-making, and constructivism are typical of less predictable situations with more uncertainty and conflict.

In normal science, consensus is more widespread and secure, while cognitive conflicts are more technical and localized. As the innovative advances are "handed down" from the research fronts, normal science tests their implications for other areas and for established knowledge, works on expanding possible applications, and tries to remove remaining conceptual and empirical difficulties (Kuhn [1962] 1970). Also, this is the area of science that is most involved in teaching and textbook writing, i.e., addressing lay audiences. Due to lower prestige and smaller stakes, competition in normal science is not as fierce as in research front groups.

Under these conditions, competition will lead to specialization (Crane 1972; Merton 1973). This is a way to avoid or restrict competition by claiming a new area of research as intellectual property, without the high risks of challenging the paradigmatic integrity of the larger field (Hagstrom 1965, 1974). The new specialties are different not so much in their paradigmatic commitments, but rather in the substantive areas and topics investigated (Mulkay 1975). That is, specialization is compatible with maintaining the overall disciplinary unity of a field. This argument explains one of the most widely observed trends in




specialty studies of scientific growth: the continuous "branching" of science into new areas of ignorance (Edge & Mulkay 1976; Lemaine et al. 1976).

Newly established specialties follow a familiar pattern of growth and decline. In the beginning, there are loosely coupled research front groups gathering around charismatic leaders who explore the potential of new problems and techniques. As the new specialty grows in size and obtains resources and property, a gradual routinization and normalization of research sets in, accompanied by increasing communication density, core journals, and organizational centers (Mulkay, Gilbert & Woolgar 1975; Mullins 1973). The core acquires a semiperiphery. At this stage, central intellectual documents express an increasingly firm consensus that is reproduced through scientific education. Productivity is high and concentrated around co-citation clusters that indicate exemplars. As the new specialty becomes more established and busy conducting normal science, the opportunities for competitive innovations decline, and so the core scientists are likely to flock out once again into new areas. The whole pattern looks like an S-shaped curve: tentative and small exploratory beginnings are followed by healthy periods of growth, only to lead to gradual decline and out-migration into new areas - starting the entire process over again.

Inside the "normal" areas of such specialties, change occurs as cumulation, or as gradual and piecemeal advances in knowledge. As opposed to permanent discovery, cumulation is a more incremental and predictable form of scientific change. Orthodox epistemology prescribes cumulation as the standard ideal for scientific progress but, again, we find it dependent on highly structured social organization with routinized practices and low uncertainty. Knowledge is cumulative only if theory changes are clearly progressive in that a new theory solves more problems and solves the same problems better than an older one, without creating more and more difficult problems itself (Freese 1980). Such progressive changes are possible only when there is firm and widespread agreement on intellectual goals, when problems are clearly defined and ordered in terms of their significance, and when alternative solutions are easy to compare. In contrast, these conditions are not present at the research fronts.

Fragmentation

Innovative research fronts and the paradigmatic routines of normal science seem to be characteristic of the more mature physical sciences, not so much of the social sciences and humanities (Price 1986). Following Kuhn ([1962] 1970), the social sciences are often characterized as "preparadigmatic," but this term connotes a problematic teleology - as if all fields of knowledge had to become scientific sooner or later. Implied in the "preparadigmatic" notion is also the view that ascribes the relative immaturity of the social sciences to their youth, but this is questionable as well (Fuchs & Turner 1986).

There can be little doubt, however, that the social sciences, indeed, differ from more mature fields. The dominant philosophical explanation for this is that there are some deep ontological differences between things natural and things social, and that two different methodologies are required to study them. Silent natural objects and their immutable laws can be explained by strong and




nomothetic natural sciences, while interpreting people who interpret themselves requires a softer and idiographic approach. From a sociological perspective, however, the difference is not in the nature of the things studied, but in the structures of the groups and organizations doing the studying. Scientific statements become strong and objective whenever they are embedded in strong networks of support; regardless of what is being studied.

The important point in this context is that the social sciences and the humanities are, for the most part, "soft" fields not because they study people who are more complex than quarks or solar neutrinos, but because they have fewer, weaker, and more dispersed resources than most of the natural sciences. In current sociology, for example, there are a great number of fairly independent and autonomous schools and perspectives (Tiryakian 1986; Tumer & Turner 1990; Wiley 1979). These are sustained by separate organizations, such as regional and specialty associations, that often control their own means of scientific production and communication; i.e., journals, newsletters, and meetings. Each of these separate organizations develops its own worldview and way of doing sociology. There is little structured exchange between the separate schools and subspecialties, and so the overall field lacks cognitive cohesiveness and social integration (Small & Crane 1979). Mutual dependence between groups is low, while task uncertainty is very high, for there is minimal agreement on anything in extremely pluralistic and diverse fields.

Strong and closely coupled organizations produce science; loosely coupled and textual organizations produce hermeneutics.15 Weak textual disciplines are more discursive and metaphysical than factual and objective. Their intellectual culture is comparatively cosmopolitan and relativistic (R. Collins 1992; Douglas 1985). There are many niches for many programs. The sheer plurality of worldviews and ideologies reveals the contingency of all worldviews. For none of the many approaches can, without strong and persistent opposition, claim special or privileged status.16 Such fields lack the strong and dense networks necessary to produce facts, and so they engage in informal conversation instead. In the form of semiotics, hermeneutics, rhetoric, and literary criticism, textual fields observe their own textuality and start believing that texts are all there is, or that the word is the world (Mulkay 1985). There is not a great deal of confidence in the possibility to become scientific and objective, and so the self- understanding of weak fields is skeptical and critically reflexive. Knowledge is seen as an active and selective social construct, rather than as a neutral mirror of reality.

Since they are not too sure about their status as sciences, weak conversational fields worry a great deal about metaphysical problems, such as the presuppositions underlying various approaches, the nature of human agency, the micro-macro problem, or the role of metatheory. Lacking unified research fronts that could define the overall direction of the discipline, weak fields do not really believe in the continuous progress of knowledge, and so there is a strong tendency to look back to the classics instead. Conversational fields merge their history and systematics. Although research fronts and conversational fields share a high amount of uncertainty, change is more focused in the former because of high mutual dependence.



Theoxy of Scientific Change / 947

How do conversational fields change? There is too much diversity and uncertainty for permanent discovery or orderly cumulation, and so there are serious doubts about the very possibility of growth and progress (B. Turner 1989). A good indicator for a noncumulative field is that it has a debate on whether cumulation is possible or not.17 The several separate literatures of such fields are comparatively unfocused and noninteractive. Books remain an important mode of scholarly communication, and the average age of cited documents is much higher than in the natural sciences (Hargens 1991; Small & Crane 1979). This situation resembles Perrow's (1984) description of organizational technosystems with low mutual dependence where disturbances can be contained within certain parts of the system. In such fields, innovations are not likely to spread throughout the entire discipline, for specialties are fairly autonomous and self-contained. Innovations that cannot be accommodated within existing specialties may be institutionalized within a new specialty, so that there is a good deal of proliferation of highly independent specialties in conversational fields. In weak fields, it is comparatively easy to create such new organizations, for the material means of scientific production are not as expensive and concentrated as in more mature sciences. In short, change will either be contained within specialties, or will lead to the further fragmentation of the overall discipline.18

When there is a great deal of task uncertainty, it is also difficult to decide what counts as innovative and progressive change, how to identify and measure this change when it occurs, and how to distinguish it from mere fads and fashions. Are postmodernism and deconstructionism progressive advances that finally dethrone the fake authority of science? Or are they degenerative symptoms of an intellectual crisis that questions the existence of foundations only to become trapped in the paradoxes of relativism? Or are they simply yet another "perspective" that will manage to carve out its own institutional niche? In weak conversational fields, change is more erratic, unstructured, and without clear direction, and innovation is often only a polite name for a faulty memory.

Despite the nature of weak fields, certain areas within them can be cumulative or even innovative in work leading to permanent discoveries. There appear to be certain specialties in sociology that come closer to the structures in mature sciences, such as mathematical sociology or experimental small group research. Note that the organizational reformulation of the traditional natural/ social dichotomy makes it possible to account for variations within the social and natural sciences that cut across the social/natural dualism. Some social sciences are stronger and more "scientific" than others, and some specialties within a given social science are stronger than other specialties within that same social science (Small & Crane 1979). In such areas, there is more faith in objectivity and intellectual progress (Berger, Wagner & Zelditch 1989). Mutual dependence may be low in the overall discipline, but is often higher in specialties.

Conclusion

What is to be gained by this structural approach to change? First, it should be possible, in principle, to predict which discoveries will turn into successful innovations. Successful innovation requires scientific marketing, or the building




of a social structure around a discovery of an invention. This is easier for core groups in closely coupled networks. Second, the theory can account for and explain the observations made in the various branches of science studies. These branches typically make isolated and localistic claims without taking much notice of each other. Once we allow for variation, however, it is possible to see, for example, that neither constructivism nor positivism reveal the "true nature" of science. Rather, the constructivist image of science as flexible negotiation and pragmatic tinkering reflects scientific work that is highly uncertain and constantly changing through permanent discoveries. The positivist image of science as rule-following is more accurate for more routinized work with low uncertainty.

It might be argued that the theory presented here captures only a small segment of science: the basic research performed in small academic specialty communities. A great deal of science is done now by complex and large "technical systems" that are made up of heterogenous sets of organizations, such as private firms, universities, and the state (Shrum 1984; Shrum, Wuthnow & Beniger 1985). Such technical systems differ from specialties in a number of ways: they are larger, more diverse, more policy-driven and goal-oriented, and more subject to administrative planning. But it appears that some of the core variables and predictions remain essentially the same when technical systems rather than specialties are examined. For example, Shrum and Morris (1990) identify competition and density as two important aspects of technical systems, and they argue, much like TSO, that uncertainty decreases the level of central- ization and formalization in such systems.

Similarly, Shrum and Wuthnow (1988) find that the reputational status of organizational sectors participating in technical systems depends, among other things, on the network position of these sectors. The more central the position, the higher the reputational status. This is consistent with the argument above: the more central one's location in the network, the more visible and consequen- tial one's initiatives and innovations.

These similarities between TSO and the sociology of technical systems suggest the comprehensive fruitfulness and explanatory power of organizational models in the study of science. At present, the field of science studies is rather indifferent or even hostile toward general theory. It appears satisfied with adding case study upon case study, and then doubting the very possibility of knowledge and representation altogether. Especially constructivist studies are too concerned with epistemological critique and philosophical puzzles such as relativism and reflexivity. The organizational approach to science might offer a way to overcome these atheoretical biases, and to link the study of science with one of the core areas in sociology, i.e., complex organizations. What is more, the organizational approach can even explain why there is so much philosophical controversy and epistemological sensitivity in sociology, and in SSK: Frag- mented and conversational fields are structurally more likely to discuss metatheoretical issues, rather than producing discoveries or facts.




Notes

1. For good critical presentations of the orthodox model, see Hesse (1980), Barnes (1974), Mulkay (1979). The recent debates in the philosophy of science are reconstructed in Laudan (1990). Recent examples of the new historiography of science are collected in Lindberg and Westman (1990). 2. For good summary critiques, see King (1971) and Restivo (1983). 3. In the sociology of science, there has been a great deal of controversy over the appropriate operationalization and unit of analysis for studies of scientific communication. The list of candidates includes "communities," "specialties," "networks," "fields," "disciplines," and "technical systems.' Part of the problem is that the ubiquity of change in science also means that the boundaries around groups of scientists are constantly changing as well. These boundaries are themselves stakes in the struggles for control over expert domains, so that they are contested and frequently ambiguous. 4. For the Neo-Durkheimian tradition, see R. Collins (1975), Douglas ([1966] 1970), Bloor (1983). For the technological paradigm, see Woodward (1965), Thompson (1967), Lawrence and Lorsch (1967), Peffow (1984), Stinchcombe (1990). Obviously, I cannot do justice here to the complexity of these traditions, and so discuss only what I perceive as their core statements. 5. The largest problem with this classification is that not all research in physics is the same. 6. This is not true for Collins's later whitings (see Collins 1989, 1992). 7. This fallacy is especially widespread in ethnomethodological studies of laboratory life (see Knorr-Cetina 1981, Lynch 1985). 8. The distinction between material and symbolic resources is analytic, not ontological. 9. For a more complete and detailed elaboration of the preceding argument, see Fuchs (1992). 10. For the case of philosophy, Collins (1992) shows that stagnation occurs when the intellectual field is very decentralized and fragmented, and when most intellectual activity consists of commentary and exegesis of classical works. He sees this condition present in the late middle ages, and in the contemporary humanities. 11. The "actor-network" perspective in SSK points out that to become visible and significant, a statement needs to "enrol" other people and statements by "translating" their interests and agendas into its own agenda: you will be able to do your work better if you follow my suggestions (see Latour 1987, 1988; Callon, Law & Rip 1986). 12. A codified methodology is the scientific equivalent to formal rules in bureaucratic organizations. 13. Again, it is important to emphasize that these material resources are also crucial at the frontiers. But they may be less of a concern there, for people who have made it into the core probably don't have to worry much about jobs and promotions. 14. Of course, all of science is uncertain to some degree, and hence no field looks like a pure Weberian bureaucracy, say, the Department of Motor Vehicles (DMV) or the Immigration and Naturalization Serivice (INS). 15. This argument is commensurable with the one suggested by Rorty (1979). Rorty also maintains that science and hermeneutics do not refer to two fundamentally different areas of culture, but express various degrees of desire for certainty. 16. There is still a "mainstream" in sociology, but it is increasingly difficult to say just what it believes in, and who is part of it. 17. See the contributions in J. Turner (1989), and the debate following Walter Wallace's proposal to resolve cognitive fragmentation by administrative fiat in Perspectives, The ASA Theory Section Newsletter, Vol. 14 (1 and 2), January and April 1991. 18. The difference between fragmentation and specialization is that specialization does not challenge the intellectual cohesiveness of the overall field.




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Article Contentsp. [933]p. 934p. 935p. 936p. 937p. 938p. 939p. 940p. 941p. 942p. 943p. 944p. 945p. 946p. 947p. 948p. 949p. 950p. 951p. 952p. 953

Issue Table of ContentsSocial Forces, Vol. 71, No. 4 (Jun., 1993), pp. 851-1120Volume Information [pp. 1113-1119]Front Matter [pp. 954-1066]Comparing World-Systems: Concepts and Working Hypotheses [pp. 851-886]The Organization of Survival: Women's and Racial-Ethnic Voluntarist and Activist Organizations, 1955-1985 [pp. 887-908]Risk and Recreancy: Weber, the Division of Labor, and the Rationality of Risk Perceptions [pp. 909-932]A Sociological Theory of Scientific Change [pp. 933-953]The Search for Adolescent Role Exits and the Transition to Adulthood [pp. 955-979]Joint Role Investments and Synchronization of Retirement: A Sequential Approach to Couples' Retirement Timing [pp. 981-1000]Racial Segregation and Black Urban Homicide [pp. 1001-1026]Religious Involvement and Self-Perception among Black Americans [pp. 1027-1055]Odds versus Probabilities in Logit Equations: A Reply to Roncek [pp. 1057-1065]When Will They Ever Learn That First Derivatives Identify the Effects of Continuous Independent Variables or "Officer, You Can't Give Me a Ticket, I Wasn't Speeding for an Entire Hour" [pp. 1067-1078]Book ReviewsReview: untitled [pp. 1079-1080]Review: untitled [pp. 1080-1082]Review: untitled [pp. 1082-1083]Review: untitled [pp. 1084-1085]Review: untitled [pp. 1085-1087]Review: untitled [pp. 1087-1088]Review: untitled [pp. 1088-1089]Review: untitled [pp. 1090-1091]Review: untitled [pp. 1091-1092]Review: untitled [pp. 1093-1094]Review: untitled [pp. 1094-1095]Review: untitled [pp. 1095-1097]Review: untitled [pp. 1097-1098]Review: untitled [pp. 1098-1099]Review: untitled [pp. 1099-1100]Review: untitled [pp. 1100-1101]Review: untitled [pp. 1101-1102]Review: untitled [pp. 1102-1103]Review: untitled [pp. 1103-1104]Review: untitled [pp. 1104-1105]Review: untitled [pp. 1106-1107]Review: untitled [pp. 1107-1108]Review: untitled [pp. 1109-1110]

Back Matter [pp. 1111-1120]

a sociological theory of scientific change

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