First Monday

Free Software/Free Science by Christopher M. Kelty


Introduction: Free Software as a Problem of Value and Reputation
Like Science
Credit and Reputation in Science
Citations and Reputations: Metaphors of Currency and Property
Conclusion: Further Thoughts on Reputation and Currency



Introduction: Free Software as a Problem of Value and Reputation

Over the last few years, as the Open Source/Free Software movement has become a constant in the business and technology press, generating conferences, spawning academic investigations and business ventures alike, one single question seems to have beguiled nearly everyone: "how do you make money with free software?"

If the question isn't answered with a business plan, it is inevitably directed towards some notion of "reputation". The answer goes: Free Software programmers do what they love, for whatever reason, and if they do it well enough they gain a reputation for being a good coder, or at least a loud one. Throughout the discussions, reputation functions as a kind of metaphorical substitute for money - it can spill over into real economies, be converted via better jobs or consulting gigs, or be used to make decisions about software projects or influence other coders. Like money, it is a form of remuneration for work done, where the work done is measured solely by the individual, each person his or her own price for creating something. Unlike money, however, it is also often seen as a kind of property. Reputation is communicated by naming, and the names that count are those of software projects and the people who contribute to them. This sits uneasily beside the knowledge that free software is in fact a kind of real (or legal) property (i.e. copyrighted intellectual property). The existence of free software relies on intellectual property and licensing law (Kelty, forthcoming; Lessig, 1999).

In considering the issue, most commentators seem to have been led rather directly to similar questions about the sciences. After all, this economy of reputation sounds extraordinairily familiar to most participants [ 1]. In particular two claims are often made: 1) That free software is somehow 'like' science, and therefore good; and, 2) That free software is - like science - a well-functioning 'gift economy' (a form of meta-market with its own currency) and that the currency of payment in thiseconomy is reputation. These claims usually serve the purpose of countering the assumption that nothing good can come of system where individuals are not paid to produce. The assumption it hides is that science is naturally and essentially an open process - one in which truth always prevails.

The balance of this paper examines these claims, first through a brief tour of some works in the history and social study of science that have encountered remarkably similar problems, and second by comparing the two realms with respect to their "currencies" and "intellectual property" both metaphorical and actual.



Like Science

The first response that could greet the above claims is an empirical one: open source is not 'like' science, it is part of science. If we ask where free software flourished in the period from 1984 to the present, then the answer is: as part of the institutions of science. From Stallman's office at MIT to Torvald's class project in Finland, the participants who make free software are drawn overwhelmingly from universities and research labs (both private and public). Furthermore, the funding that supports many projects (in most cases indirectly) comes from those well-known scientific institutions such as: national science funding agencies, university operating budgets, royal academies, government funding agencies and research labs, industrial R&D labs and non-profit research organizations, governmental and non-governmental science agencies, and private research and development institutes.

If we trace the roots of free software (prior to Stallman's FSF) and generously include the work of AT&T Bell Labs, Sun, and UC Berkeley on Unix - which arguably benefited from similar dynamics of open source code and shared projects, even if it was not free in the strong sense - then the case is undeniable: free software is as much a part of science as computer science and engineering have been.

And as part of the infrastructure of science, it should not be surprising that its circuit of value looks remarkably similar to that of science. So why not ask the analagous question - "how do you make money with science?"

Perhaps this question seems more familiar. The importance of science and technology to industry is presumed to be a fact of life - a relationship (basic reseach leads to applied research leads to profits, funds, and growth) whose direction of influence is questioned only in the doing. Making the comparison between free software and science gives us the chance to ask anew - just how is it that anyone makes money from the results and products of science, when scientists just give them away for free? Even though we know that science is not free from the ebb and tide of corporate capitalism, or of national political interest, should this not be an equally perplexing question?

What is interesting about this comparison is that openness, whether in science or in free software, is not assumed to be moral, but rather structural, and here an interesting conundrum appears:

When open source and free software advocates compare free software to science, and the scientific method, they usually make the claim, often explicitly, that through some unspecified mechanism this open, collaborative, non-proprietary community of software development actually results in better software, whether indirectly through debugging or directly through openness and deliberative design. Indeed, even across the lines drawn between them, the Open Source organization and the Free Software Foundation agree on this: guaranteed openness creates better, more stable, less buggy software - software that does not suck [ 2].

However, when scientists are confronted with the sociological insight that the socio-technical and legal structure of science might affect the quality, or more importantly the criteria and determination of the truth of their results, the reaction is universally one of violent denial. In this case social structure is assumed to be, at best, like friction on scientific truth. Openness plays instead a supporting role in most scientists' accounts: without it, some important scientific truths might be suppressed, ignored, or abused by bone-headed bureaucrats and dim-witted politicians, but it would never be rendered less true. Making science that does not "suck" has little to do with such pedestrian concerns as intellectual property or the conventions and norms of practice.

So what's going on here? If free software and science have shared the same technical and social structure of collaboration and dissemination for the last 20 years (longer if you count the creation of the Internet itself), then why would the culture of openness, collaboration, and sharing result in creating or improving high quality complex software, when the same system's effect on scientific research is assumed to be negligible?

Answer one is that it doesn't: free software is not better, or its quality does not flow from the structure of collaboration. Good software, like scientific truth, comes from elsewhere, though no one's saying where just yet.

Answer two is that it does: the cumulative progress depends on an institutional and conventional structure of openness and peer review to function, but that it is only a necessary and not a sufficient condition of truth. A sine qua non if not a causa efficiens of truth.

Both of these answers concerning the role of the institutional media are interesting - the first errs in the direction of self-interest and altruism, the second in the direction of collective consciousness and institutional determinism. In both cases we can query the role of reputation and credit: as part of the dynamics of interaction amongst individuals with differing and partial motivations, as part of the technical communication stucture in which they perform, and as part of the social and legal structures that enable and constrain their action. It is in these terms that research in the history and sociology of science and scientific thought can help draw some distinctions that may well help illuminate the supposed conundrum of "how to make money with free software".



Credit and Reputation in Science

The locus classicus for the scientific study of scientific institutions is Robert K. Merton, the American sociologist who first attempted to think through what he called the "normative structure of science" - a sociological account of scientific action that focused on the "reward system" and the "ethos" of science [ 3]. For the former Merton explicitly dubbed such rewards "intellectual property" even though the term could not but be used in a metaphorical sense. The latter, the "ethos" of science, is that set of norms and forms of life that structure the activity of scientists across nations, disciplines, organizations or cultures. It bears closer scrutiny as a comparison with free software (especially in its international, cross-organizational,multi-disciplinary forms). Merton identified four norms: universalism, communism, disinterestedness, and organized skepticism.

These norms are informal, which is to say, they are only communicated to you by becoming part of the scientific establishment - they are not written down, and are neither legally nor technically binding. However, despite this informal character, the institutions of science as we know them, are formally structured around them: e.g. Communism requires a communication structure that allows the communally owned property - ideas, formulae, data, or results - to be disseminated: journals, letters, libraries, university postal systems, standards, protocols, and some more or less explicit notion of a public domain [4].

The norm "disinterestedness" is not an issue of egoism or altruism, but an institutional design issue: for disinterestedness to function at all, science must be closed off and separate from other parts of society, so that accountability is first and primarily to peers, not to managers, funders or the public - even if this norm is continually under assault both from within and without. Similarly, "organized skepticism" is not simply methodological (whether cartesian doubt or acceptible 'p' values), but institutional as well - meaning that the norms of the institution of science must be such that they explicitly, if not exactly legally, promote the ability to maintain dissent even in the face of political power. Otherwise, truth is quickly compromised.

What remains most fascinating about Merton's descriptions of science was his focus on rewards, and on the "intellectual property system" of science. Merton and his students explained how the reward system of science functions as an incentive to scientific creativity, indeed as an essential structural aspect of the cumulative nature of science, even though there are no legal, technical and only thin institutional means for enforcing ownership.

One such means for enforcing propriety is the priority dispute. Priority disputes, beginning most prominently with Galileo, are often prolonged and intense quarrels, not because scientists are inherently egotistical, but because fairness and disinterestedness are essential to the legitimacy of the enterprise. In many of these cases, the entire technological, institutional and intellectual structure of scientific argument become starkly visible, complicating the simple story of determining "who was right". As the institution of science has grown larger and more complex, systems for managing priority, and reward have also developed: journals and their reputations; international standardization of measurements, constants, experimental apparati; Nobel prizes and their kin; and, the university system.

To be clear, these systems have not developed with the a priori goal of measuring the reputation of scientists, but under other, sometimes plain, sometimes conflicting, ideals such as "promoting progress" or "curing disease" or "the pursuit of truth" in which the story of scientific advancement is assumed to be obvious. It is only with a certain kind of sociological hindsight that these systems appear to have a more complex structural and functional purpose; or that the norms that keep the systems running can be seen.

One might draw a parallel here with Hackers and software developers who often insist that the best software is obvious, simply because "it works". The implicit claim is that software, like scientific truth, is "obvious" and requires no discussion (and is independent of "our" criteria). While it is true that incorrectly written software simply will not compile, such an insistence inevitably glosses over the negotiation, disputation, and rhetorical manuevering that go into convincing people that, for instance, there is only one true editor: Emacs. To simply suggest that a scientific result is true because truth is obvious is both sociologically and scientifically simplistic. The process of declaring such truth is tedious, arcane, and terribly fascinating to most scientists, but it is by no means obvious. Breast-beating never achieves consensus in science, only suspicion [5].

To reiterate, Merton focused on the institutional norms of science, rather than the character of individual scientists, or any categorical imperative to pursue the truth [ 6]. Merton's assertion was that recognition and reputation play the crucial roles in the incentive structure of science: eponomy (the naming of constants, laws and planets),paternity (X, Father of Y), honors, festschrifts, and other forms of social recognition, prizes like the Fields medal or the Nobel, induction into royal societies, and ultimately "being written into the history books." Again, these mechanisms are functional only in hindsight; it is perhaps possible to say that science would still proceed without all these supports, but it would have neither collective existence in nor discernable effect on the historicalconsciousness and 'vocational' identity of practicing scientists.

The extensions and arguments that have followed Merton's pioneering works have centered around this important methodological question: how can we understand the dynamic structure of the growth of scientific knowledge through the empirical study of its institutions and norms, as well as its tools, practices and ideas?

The well known book by Thomas Kuhn, The Structure of Scientific Revolutions, attempted to make this investigation coincide with the structure of the cognitive content of science. "Paradigm Shifts" have entered the language even of the scientists themselves as a way to explain how certain extraordinary cognitive reversals can be described by the apparati of proof and explanation, and in particular, their "incommensurability". Though Kuhn vigourously denied that the social institutions of science have anything to do with these shifts, his general approach to the problem begins in sympathy with Merton.

Similarly, Michael Polanyi introduced a notion of "tacit knowledge" to account for the aspects of cognitive differences that were not made explicit, that were, so to speak, embedded within machines, techniques, and scientific training. These tacit skills vacillate between the explicit and the implicit - they have real existence in the world, especially in a pedagogical sense and they are institutional even if they are ephemeral - they are not laws, or rules, but learned habitual actions without which science would never get done.

In looking to the historical origins of the institutional structures of science, Simon Schaffer and Steven Shapin took Robert Boyle and Thomas Hobbes' debates about the experimental method as an experimental site for investigation of the technologies involved in the production of scientific truth. They conclude that "proving" something, in this case, required multiple expertises: literary, technical/mathematical, and material or craft skill and further that the creation of an argument (in this case as to the existence of a vacuum) also required the creation of an audience of witnesses who could be made to testify to the truth. The work of creating a scientific fact is never simply the problem of convincing others - after all, Galileo was still labeled a heretic, despite his scientific skill and social savoir-faire.

Credit and reputation studies enter the literature explicitly in the late 1970's. Gift economies, in particular, were the study of a short article by Warren Hagstrom that attemted to explain how the contributions to scientific research - such as 'giving' a paper or crediting others - made the circulation of value an issue of reciprocity approximating the gift-exchange systems explored by Marcel Mauss and Malinowski (see Hagstrom, 1982). Bruno Latour and Steve Woolgar also explore the metaphors of non-monetary exchange in science, in the course of their work on the construction of facts in laboratories. They explore what they call a "cycle of credit" that includes both real money from granting agencies and the recognition (in the form of published articles) that leads full circle to the garnering of grant money, and so on ad infinitum. In this cycle, both real money, and the currency of reputation or credit, are mobilized - but to what end exactly, they do not venture (Latour and Woolgar, 1979).

In each of these cases, the function of reputation or credit is the same: it is the most valuable and deserved marker of success, and thereby of the trustworthiness of scientific results in an inherently untrustworthy world [ 7]. The promise of being "written into the historybooks" or at least the guarantee that your work will not have been in vain, is the only identifiable motivating force - as such, reputation can be called the incentive of scientific work. It is from this assumed relation that the metaphor of "intellectual property " gains its force, and seems an obvious way to approach the "economic " problem of science.

However, it is important to distinguish the "metaphorical" from the "literal" use of intellectual property: in the case of the scientist, reputation is inalienable. No one can usurp a reputation earned, it cannot be sold, it cannot be given away. It may perhaps be shared by association, it may also be unjustly acquired - but it is not an alienable possession. Intellectual Property granted by a national government, on the other hand, exists precisely to generate wealth from its alienability: inventors, artists, writers, composers can sell the products of their intellectual labor, transfer the rights to commercialize it, in part or in whole, by signing a contract. The reputation of the creator is assumed to be separate from the legal right to profit from that creativity. This legal right - intellectual property as a limited monopoly on an invention or writing - is often confused with the protection of reputation as an inalienable right to one's name; this, intellectual property law does not protect [ 8].



Citations and Reputations: Metaphors of Currency and Property

The metaphors of currency and property in science meet in a peculiar place: the Science Citation Index. Citation indices give one very prominent if not always precise, indicator of value. Even though all reputation does not depend on citations (though some tenure committees and granting agencies beg to differ on this point), work that isn't included in such databases is at a rather serious disadvantage, reputationally speaking. That is to say, science citation indexes do not simply measure something objective (called reputation) but rather give people a tool for comparative measure of success in achieving recognition.

Robert Merton clearly understood the power of citation indexing - both as currency, and as a kind of registration of intellectual property for the purposes of establishing priority. In the preface to Eugene Garfield's 1979 book Citation Indexing, Merton says "[Citations in their moral aspect] are designed to repay intellectual debts in the only form in which this can be done: through open acknowledgment of them" [ 9]. He thus makes of citations the currency of repayment. But he goes even further, explaining scientific intellectual property in a manner that directly parallels the claims made for free software's success as a reputation economy:

"We can begin with one aspect of the latent social and cultural structure of science presupposed by the historically evolving systematic use of references and citations in the scientific paper and book. That aspect is the seemingly paradoxical character of property in the scientific enterprise: the circumstance that the more widely scientists make their intellectual property available to others, the more securely it becomes identified as their property. For science is public not private knowledge. Only by publishing their work can scientists make their contribution (as the telling word has it) and only when it thus becomes part of the public domain of science can they truly lay claim to it as theirs. For the claim resides only in the recognition of the source of the contribution by peers" [ 10].

This is a remarkable claim, but not dissimilar to that remarkable claim of free software advocates - that openess results in the creation of better software. Merton here claims as much for science. The incentive to produce science depends on the public recognition of priority. The systems involved in making this "property" stick to its owner are reliable publishing, evaluation, transmission, dissemination and ultimately the archiving of scientific papers, equations, technologies, and data. As above, this priority is inalienable: when it enters this system of registration, it is there for good, dislodged only in cases of undiscovered priority or hidden fraud. It is not alienable intellectual property - but constant, irretrievably and forever after granted.

"Intellectual Property" in these metaphorical terms, references a curious problem of sovereignty: who grants scientific intellectual property, who guarantees it? The equally curious answer is that no one does: science is only ever an ingenious evolution - a foundationless but stable system of openness. It is public, not private knoweldge, as Merton says. Clearly, its gyroscopic continuation depends on a constant injection of angular momentum - usually in the form of money, sometimes in the form of political support, pressing demands and utopian desires - by universities, philanthropists and other patrons. Its norms readjust over time, credit circulates, the reputations of both individuals and funders grow and the legitimacy of the enterprise continues. Much like the claims made for free software, the distributed peer-to-peer production and cross-monitoring of science is a stable, if anarchic, way to create and double-check the highest quality material. The 'progress' of science knows no other way.

However, what is perhaps most interesting about Merton's statement today is that there is no longer (if there ever was) any institutional guarantee of the public nature of science. In fact, precisely the opposite is true today: the openess or public nature of knowledge cannot be assumed, but must be asserted in order to be assured. Where scientists once raced each other to publish data, they now race to patent it. Science remains "public" in the sense of 'not secret', but it also enters a stage of being private intellectual property first, public scientific research second. This is neither ethically nor historically mysterious, but indicates instead that the very institutional substance of science has shifted over the last 30 or so years. It has shifted onto a terrain of massive corporate investment (as opposed to either government or non-profit foundation investment) in research activities - an investment governed by an explicit structure of quantifiable returns (which often but not always means monetary returns). Even in the case of government and public funding, this organizational demand of corporate-style accounting and measurment of returns dominates scientific work almost down to its core. Science and its results are now property, and the returns on this property are not subject to peer review.

Further, this shift has been accompanied by a massive expansion of the actually existing intellectual property regimes [ 11] to cover things that scientists previously did not seek to protect (algorithms, genes, processes, tools). This expansion has at the same time shrunk the public domain of scientific information and the ethic of unrestricted common access in the name of profiting from "information goods" - i.e. using the production of intellectual property as an incentive for investment in research has trumped the need for free-flows of information as a component of scientific research. The result has been a sometimes profitable, but always extraordinairily expensive system of pervasive licensing and cross-licensing of intellectual property [12].

During this same period, scientific research has also seen the massive growth of heterogenous computer networks (of which the Internet is the most prominent - and most open - example) that link research fields both intensively and extensively and provide the possibility for a kind of peer-review that precedes, and occassionally bypasses [ 13] the existing, trusted, and well-known peer-review of scientific journals and societies. After all, the Internet was a scientific research network before it ever saw acommercial dollar - and the scientific research establishment has come to depend on it as central to its institutional structure.

All this indicates, in short, that the entire material and normative structure of science has taken a turn that has three very interesting implications:

  1. "intellectual property" is no longer only a metaphorical description of the things scientists produce, but refers anew to what it was metaphorically derived from: actually existing (internationally enforced and legally guaranteed) intellectual property. Reputation may not yet be for sale, but it is today always accompanied by a contract.
  2. Scientists are forced to explicitly consider the trade-off between the ownership of data, information, or results and the legal availability of them. Whereas in its metaphorical incarnation, "intellectual property" required only that the "licensee" give proper credit to the owner, the current situation increasingly requires both the attribution of credit, and an explicitly quantified monetary payment to the legal owner (who is only rarely the scientist). Science is no longer "public knowledge" but publically visible privately owned knowledge. The race to publish is now often also a race to patent.
  3. The "Internet" such as it existed prior to around 1994 not only embodied but formed the basis for an institutional structure of openess in science. Anonymous access and unrestricted availability define both the science and the technical structure of the first twenty or so years of the Internet. Whether such a "research" Internet can continue depends very much on the outcome of the continuing privatization of the Internet, including the transformation of its very architecture [ 14].

Interestingly, these three implications apply equally well to free software. In fact it is out of just such a situation that the Free Software License emerged: the situation of software creation in the late 1970's and early eighties when the heart of one of the most scientific research establishments in the world, the MIT Artificial Intelligence Lab, was cut out to provide a handful of competing companies with intellectual property (in the form of personnel) [ 15]. Since that time, nearly all free software programmers have been forced to come to terms, so to speak - with the absolutely essential nature of the license - without it, software can be made proprietary again. Unfortunately, there are very few scientists who realize something similar about science. There is as of yet, no such thing as "Free Science".



Conclusion: Further Thoughts on Reputation and Currency

If the informal currency of science is the citation, this is certainly not unknown to the free software world. Even Eric Raymond's highly fantastic metaphorical treatment of reputation recognizes that there is an informal convention concerning the list of contributors to a project: that it should never be modified by subsequent users [ 16]. Similarly, Rishab Ayer Ghosh and Vipul Ved Prakash (Ghosh andPrakash, 2000) combined this informal convention with the formal availability of software packages to create a tool that could do a rough estimate of what a citation index is: add up all the mentions by grepping packages for e-mail addresses and copyrights. We might call what they find "greputation" since it bears the same relation to reputation that money does, supposedly, to value. That is, it is the material and comparable marker of something presumed to be more complex - the reputation of a scientist - just as money is anarbitrary technology for representing value.

We might even take this analogy a bit further, and ask just what that relationship between money and value is, and if we can say something similar about the function of reputation in both science and free software. Economic dogma has it that money is a standard of value. It is a numerical measure that is used to compare two or more items via a third, objectively fixed measure. This is an unobjectionable view - unless one wants to ask what it is that people are doing when they are valuing something.

From the perspective of Georg Simmel, the early 20th century German sociologist whose magnum opus is devoted to the subject (Simmel, 1990), considering money as something that simply facilitates a natural human tendency (to value things according to cardinal ranking) is a sociologically and anthropologically illegitimate assumption. Humans are not born with such an objective capacity vis-à-vis the world around them. Rather, since money is a living set of institutions that calibrate value and a set of technologies (cash, check, credit, etc.) that allow it to circulate or accumulate, then humans are caught within a net that both allows and teaches them how to reckon with money - how to count with it, as well as on it. Even staunch neo-classicists agree that the rational actor of economic models does not exist. However, that by no means suggests he can not be brought into existence by the institutions of economic life. To borrow David Woodruff's willful anachronism: "humans are endowed only with an ordinal sense of utility; they attain something like a cardinal sense of utility ("value") only through the habit of making calculations in money."

If we consider this insight with respect to the "currency" of reputation, as well as that of money, we can say the following: the standard of value (money, or the citation) only serves to stabilize the network of obligations thus created: in the case of money economies, a single cardinal value, in the case of citations, a widely recognized, though oft disputed reputation. The vast interconnected set of legal obligations that money represents can be universally accounted by a single standard - a cardinal value. But if we reckoned the world of obligations using a different standard - a non-numerical one, for instance - then humans could also learn to express utility and value in that system. Money, it should be very clear, simply isn't natural.

Therefore, a similar approach to scientific citations would have to focus on something other than their cardinality (except, as I mentioned, perhaps in the case of tenure reviews, where only quantity seems to count). In fact this does happen. Citations are not always simply good, they can be bad and indifferent as well. Some things become so well known that they are no longer cited (F=ma, natural selection), but this could hardly diminish the reputation of their progenitors. Rather the skill with which academics can read the language and subtleties of citations means they learn how to express gratitude and repay intellectual debt in similarly standardized, though not simply quantitative ways.

Equally in free software. Although some might like to suggest that good software is obvious because "it works," most programmers have deep, abiding criteria for both efficiency and beauty. Leaf through Donald Knuth's The Art of Computer Programming for a brief taste of such criteria and the interpretive complexity they entail. To fail to reckon value according to similar criteria leads only to exclusion from the network.

The scientist who does not cite, or acknowledge, incurs irreconciliable debts - debts that cannot be reckoned in the subtle currency of citations. The curious and difficult problem then becomes: how do networks like science recruit people and grow in size in order to force the standard of value to work accross long distances and over significant times? The answer, perhaps: the same way money does, through the violence of exclusion. The more legitimate the information infrastructure of scientific publications, databases, and history books becomes, the more essential it is to play by those rules, or find increasingly creative ways to break them. In money as in science, to refuse the game is to disappear from the account.

The question that opened this paper - "how do you make money with free software" - most often gets referred back to the value of reputation, and whether that reputation can be converted to money. In a strange way then, we are led to ask whether the "currency" of reputation is actually in some kind of competition with money. On the one hand the answer is yes: both currencies are equally capable of expressing value, and could be used to reckon a network of obligations, perhaps even converted into each other. On the other hand, and more interestingly, the answer is no: reputation reckons these obligations according to a much richer and far more flexible metric. It provides participants with a language in which to argue about value as well as assign it. Money teaches us to count, but science, inasmuch as it is not governed by money, might yet teach us to think. End of article


About the Author

Christopher M. Kelty graduated from MIT with a Ph.D. in the History and Social Study of Science and Technology; his dissertation explored information standards and entrepreneurial capital in U.S. healthcare. For the past two years he has been doing research on free software in Germany, the U.S., and India. This fall, he joined the faculty in the Anthropology Department at Rice University in Houston.



A version of this paper was presented at the first First Monday Conference at the International Institute of Infonomics in Maastricht, the Netherlands, on Monday, 5 November 2001.



1. See in particular, the introduction to Dibona, 1999; and Eric Raymond's "The Magic Cauldron," 1999a.

2. See esp. Raymond, 1999a; 1999b; and, DiBona et al., 1999.

3. See, generally, Merton, 1973.

4. The social sciences more generally have a number of names for this informal norming, which constitutes an intensive focus of the social and anthropological theory of the last 150 years - some that may be familiar include Weber's focus on 'vocation' or 'calling', Durkheim's work on organic and mechanical solidarity in the division of labor, the American Pragmatist tradition (from Peirce's 'habit' to Mead's 'mind') and its concern with the working out of ambiguity though constant co-monitoring of selves, Bourdieu's habitus, Foucault's concern with ethics as self-fashioning, so forth. Merton's version is a conservative and somewhat brittle version of this norming function, but it is meant to capture only the empirical forms of "ethics" in the sciences of the 19th and early 20th centuries and not their conditions of possibility.

5. For an account of science told from this perspective, see Latour, 1986.

6. It was Max Weber, in Science as a Vocation in 1918, who first connected the subjective with the institutional in science.

7. On the relation between truth and trust see Shapin, 1994; and, with respect to the role of the printed book, Johns, 1998.

8. Again, in this context, the study by Adrian Johns of piracy and the culture of the book makes plain that the relationship between these two forms is by no means simple, and that the distinction between outright plagiarism and debates about priority of discovery had to be created - the implication being that the assumption that propriety is an issue of authorship, invention or discovery rather than a commercial one is not obvious, but must be actively maintained to make sense. See Johns, 1998.

9. Garfield, p. viii.

10. Garfield, pp. vii-viii.

11. see James Boyle's work at, esp. on the "New Enclosure Movement".

12. Until very recently, there has been almost no focus on this evolution. Recent work by Reichman and Uhlir, 2001 has begun to address the issues of a scientific "public domain".

13. For example, the Los Alamos preprint server

14. See. e.g. Lessig, 1999.

15. See Levy, 1984.

16. See Eric Raymond, "Homesteading the Noosphere", 1999b.



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Editorial history

Paper received 14 November 2001; accepted 16 November 2001.

Contents Index

Copyright ©2001, First Monday

Free Software/Free Science by Christopher M. Kelty
First Monday, volume 6, number 12 (December 2001),