In a book devoted to inviting philosophers to join in technosocial activism, academic philosophers of science would seem to be a most unreceptive audience.  It is not that philosophers of science think that nothing they have to say is relevant to social responsibility.  Alex Michalos (1984), though intially reluctant, did end up—in a widely used summary of philosophy of science—finding areas of social responsibility relevance.  And many of the most traditional positivist philosophers of science (as explicitly stated by Reichenbach, 1951) saw their role as defenders of the objectivity of science, which they simply assumed was progressive or socially beneficial.

    I am aware of only one famous foray by a philosopher of science into social activism of a sort:  Michael Ruse's serving as an expert witness in the 1981-1982 creation science trial in Arkansas; and most philosophers of science at the time thought of that foray into activism as a disaster (see LaFollette, 1982, and Ruse, 1982).
 In my experience, most philosophers of science today—even as the field has become hopelessly fragmented (see chapter IX, above, and Durbin, 1994)—are satisfied to argue with one another in about as inbred a fashion as the most academic of academicians.  Even so, I want here to issue a call to activism to them as much as any other philosophers.

    One often hears, in philosophy of science as well as other intellectual circles, nasty put-downs of opponents.  Philosophers of science opposed to Thomas Kuhn do not simply object to what they see as his relativism; they get angry about the matter.  (See, for a humorous though serious example, Laudan, 1990.)    Similarly, Joseph Pitt (1990), reviewing the introductory textbook, Philosophy of Technology, does not just object to Frederick Ferre's approach; he feels the need to use harsh words.  And the same phenomenon occurs everywhere in scientific and technological literature, with attacks on others as quacks or charlatans, as "just plain wrong," and so on (See Radner and Radner, 1982.)

    I understand the passion for truth, the insistence on rooting out error, that motivates these exchanges.  I also understand the motives of recent skeptics who challenge the grounds on which people take their stand in making such judgments.  The issue turns on whether one thinks it is or is not possible to discover the truth or some warranted-assertability approximation to the truth.

    I want to sidestep that issue here.  The key word in doing so is "discover."  People often claim to have discovered the truth or to have uncovered an error or a mistake.  I do not want to focus on such (alleged) discoveries, but on discovering, on the process of discovery, on the doing of science (including biomedical science, science in an engineering context, and other areas of technical work).

    In that arena, it seems to me, there is much more room for a cooperative attitude, for working together, for seeking commonalities.  Focusing on this, with respect to science and technology policy, emphasizes that these noble endeavors are, above all, human projects.  They only work well if the individuals involved share motives and knowledge bases, communicate, critique one another's work constructively, and generally collaborate in a common enterprise.  And if this is done well—scientists have always assumed—society will benefit, at least in the long run.  Here I want to shorten that long run, and to focus on possible contributions of philosophers of science rather than the scientists they study.

    Given the general inclination of philosophers to concentrate on warranted assertions, it might come as something of a surprise to discover how much has been written, in recent decades, on the discovery process.  There is, of course, the now-vast literature in what is sometimes called the "sociology of scientific knowledge" (see chapter IX, above), and I will refer here to some authors in that tradition (those traditions).  But I refer to a variety of authors from other traditions as well.  I have chosen just a small sample, but I have tried to make it representative of the whole field of science, from abstract mathematics through the physical and natural sciences to engineering.

    The first major figure to emphasize the discovering process was the mathematician George Polya, beginning with his popular and influential handbook, How To Solve It (1957[1945]).  In 1954, Polya published a two-volume study, Mathematics and Plausible Reasoning, where he says:
 

    Mathematics is regarded as a demonstrative science.  Yet this is only one of its aspects.  Finished mathematics presented in a finished form appears as purely demonstrative, consisting of proofs only.  Yet mathematics in the making resembles any other human knowledge in the making.  You have to guess a mathematical theorem before you prove it; you have to guess the idea of the proof before you carry through the details.  You have to combine observations and follow analogies; you have to try and try again.  The result of the mathematician's creative work is demonstrative reasoning, a proof; but the proof is discovered by plausible reasoning, by guessing.  If the learning of mathematics reflects to any degree the invention of mathematics, it must have a place for guessing, for plausible inference (p. vi).

    The sociological historian Andrew Pickering is well known as one of the most extreme advocates of the so-called "strong programme" in sociology of science.  He says (Pickering, 1984, p. 12) the key to his analysis of the dynamics of research traditions in high-energy physics is a theme he calls "opportunism in context":  "Research strategies," he says, "are structured in terms of the relative opportunities presented by different contexts for the constructive exploitation of the resources available to individual scientists."  Some of the limited resources that constrain practice are material, such as major pieces of equipment available only at certain laboratories.  But Pickering focuses even more on theoretical resources:

    The most striking feature of the conceptual development of HEP [high-energy physics] is that it proceeded through a process of modelling or analogy.

Two key analogies were crucial to the establishment of the quark-gauge theory picture.  As far as quarks themselves were concerned, the trick was for theorists to learn to see hadrons as quark composites, just as they had already learned to see nuclei as composites of neutrons and protons, and to see atoms as composites of nuclei and electrons.  As far as the gauge theories of quark and lepton interactions were concerned, these were explicitly modelled upon the already established theory of electromagnetic interactions known as quantum electrodynamics.

The point to note here is that the analysis of composite systems was, and is, part of the training and research experience of all theoretical physicists.  Similarly, in the period we will be considering, the methods and techniques of quantum electrodynamics were part of the common theoretical culture of HEP.

Thus expertise in the analysis of composite systems and, albeit to a lesser extent, quantum electrodynamics constituted a set of shared resources for particle physicists.  And, as we shall see, the establishment of the quark and gauge-theory traditions of theoretical research depended crucially upon the analogical recycling of those resources into the analysis of various experimentally accessible phenomena (Pickering, 1984, p. 12).

    Ronald Giere has been recognized for two decades as a leader in philosophy of science focusing on the foundations of probability and statistical inference.  Grieve (1988, p. xvi) now thinks his earlier approach was mistaken:

    My skepticism [has] progressed to the point that I now believe there are no special philosophical foundations to any science.  There is only deep theory, which, however, is part of science itself.  And there are no special philosophical methods for plumbing the theoretical depths of any science.  There are only the methods of the sciences themselves.

    An evolutionary model of science grounded on natural, cognitive mechanisms removes any need to feel apologetic in the face of the obvious fact that the approach to a scientific issue adopted by individual scientists often seems more determined by the accidents of training and experience than by an objective assessment of the available evidence.

This is just what one should expect of normal cognitive agents.  What sorts of models any individual will regard as most promising or appropriate will of course be strongly influenced by which sorts of models have been learned first and used most.

This is not irrationality or anything of the sort.  It is normal human behavior, and scientists are normal human beings.  Nor does this imply a relativist view of science.  The right kinds of interactions among scientists favoring different approaches, together with extensive interactions with nature (mediated by appropriate technology), can produce wide-spread agreement on the best available approach.

    Under this heading, I want to cite two examples.  The first is the obvious place to start, with James Watson's story in The Double Helix (1968).  Watson begins that famous account with a preface:

    Here I relate my version of how the structure of DNA was discovered.  In doing so I have tried to catch the atmosphere of the early postwar years in England, where most of the important events occurred.  As I hope this book will show, science seldom proceeds in the straightforward logical manner imagined by outsiders (Watson, 1968, p. ix).

Instead, its steps forward ( and sometimes backward) are often very human events in which personalities and cultural traditions play major roles.  To this end I have attempted to re-create my first impressions of the relevant events and personalities rather than present an assessment which takes into account the many facts I have learned since the structure was found.  Although the latter approach might be more objective, it would fail to convey the spirit of an adventure characterized both by youthful arrogance and by the belief that the truth, once found, would be simple as well as pretty.

Thus many of the comments may seem one-sided and unfair, but this is often the case in the incomplete and hurried way in which human beings frequently decide to like or dislike a new idea or acquaintance (Watson, 1968, p. ix).

    Recently Karin Knorr-Cetina, a leader among the new breed of sociologists of science, has (together with Klaus Amann) carried out a fascinating series of studies on image analysis, one of the keys to the discovery of the structure of DNA by Watson and Francis Crick, and especially Rosalind Franklin.  Knorr-Cetina and Amann (1990, p. 259) begin one report of their recent studies with the observation that, "Philosophers, historians, and sociologists of science have long considered writing to be a central part of scientific activities."  They admit this is as it should be but add:  "Yet from within scientific inquiry, the focus of many laboratory activities is not texts, but images and displays."

    Knorr-Cetina and Amann then concentrate on talk about images in the laboratory related to four environments:  laboratory practice, invisible physical reactions, the image as it will appear in future publications, and case precedents in the field.

    Knorr-Cetina and Amann (1990, p. 281) conclude:

    Image surface calculations, reconstructions of events in the test tubes of the lab, and remedial actions designed to transform badly turned-out pictures into showcases of data exemplify the type of work performed when technical images are inspected in the laboratory.  Suffice it to add that many autoradiographs or other images are not just inspected once, but give rise to several image-related conversations, and many images are internally related by being predecessors or successors of others.  Thus, instead of looking more or less directly at the laboratory and glossing the invisible processes therein, participants looked first at other pictures and let themselves be guided to these processes by the appearance of the pictures.

    My example here may be less appropriate than others in this listing.  David Hull's Science as a Process:  An Evolutionary Account of the Social and Conceptual Development of Science (1988) reinforces fairly traditional sociological accounts of science as a reward system (see Gaston, 1984), and it claims to describe natural science as a whole rather than just the life sciences.  But the marvelously detailed accounts of competition that Hull includes focus on biologists as the communities of scientists he knows best.  As his title indicates Hull's book concentrates on the real-life process of science, not some philosopher's abstraction.

    Hull ends with the claim that his book was intended as the fulfillment of Thomas Kuhn's and Stephen Toulmin's earlier projects.  Here is a sample of Hull's rhetoric (1988, p. 7) on the way scientists compete:

    In science, "weasel words" serve an important positive function.  They buy time while the scientists develop their positions.  It would help, one might think, if scientists waited until they had their views fully developed before they publish, but this is not how the process of knowledge development in science works.  Science is a conversation with nature, but it is also a conversation with other scientists.  Not until scientists publish their views and discover the reactions of other scientists can they possibly appreciate what they have actually said.

    Under this heading, I cite three authors.

    i)  The first, Daniel Rothbart, does not depart much from traditional approaches in philosophy of science; the article I quote from (Rothbart, 1984) is filled with references to reference, meaning, and "semantic field theory."  But Rothbart's conclusion -- that metaphor is "an essential aspect of scientific reasoning" -- even he says is at odds with one of the deepest prejudices positivistically inclined philosophers of science have held, namely, that metaphors, though indispensable in science, are always ultimately explicable in literal terms.

    Rothbart's conclusion (p. 611) is based on the treatment of several examples using this model:

    The function of metaphoric projection is to reorganize the semantic field by introducing new saliencies into the field by highlighting some features and eliminating others.  New attributes are formed and can be directly beneficial when a conventional field of concepts fails to permit certain desirable features to emerge. . . .

    If metaphor forms the basis of concept formation, then conceptual problem solving is in many cases fundamentally metaphoric.  Assuming that a conceptual problem is some weakness within the system of concepts, the gain from metaphor is expansion of the range of possible features attributable to the subject.  This range was apparently too limited with the subject's own semantic field.  When the primary subject is juxtaposed with prototypes from an alternative field, the metaphoric projection causes a reformulation of the network of similarities and differences.

Although metaphoric projection would not by itself validate a given hypothesis, it becomes a matter of rational preference for scientists to reformulate problematic concepts through metaphor.  Its epistemic value arises from expansion of available similarity features (Rotherbart, 1984, p. 611).

    Holton prefaces one of his studies (1978, p. vii) this way:

    Considering the progress made in the sciences themselves over the past three centuries, it is remarkable how little consensus has developed on how the scientific imagination functions.  Speculations concerning the processes by which the mind gathers truths about nature are among the oldest and still most prolific and controversial cognitive productions.  Unless the inevitable distortion of near perspective is misleading me, it appears that only in the relatively recent period have proposals been made that have long-range promise.

    The chief aim of this book is to contribute concepts and methods that will increase our understanding of the imagination of scientists engaged in the act of doing science.

    A finding of thematic analysis that appears to be related to the dialectic nature of science as a public, consensus-seeking activity is the frequent coupling of two themata in antithetical mode, as when a proponent of the thema of atomism finds himself faced with the proponent of the thema of the continuum. . . . The persistence in time, and the spread in the community at a given time, of these relatively few themata may be what endows science, despite all its growth and change, with what constant identity it has.  The interdisciplinary sharing of themes among various fields in science tells us something about both the meaning of the enterprise as a whole and the commonality of the ground of imagination that must be at work (1978, pp. 10-11).

    iii)  It is the philosopher Nicholas Rescher, however, who has gone farthest along these lines in his interpretation of the nature of science.  I have in mind especially Rescher's book, Dialectics:  A Controversy-Oriented Approach to the Theory of Knowledge (1977).

    Rescher makes a complex case for his view.  I cite here only a few short passages from his concluding chapter:

    This final chapter will explore the prospects of devising a disputational model for scientific inquiry.  The basic idea of such a model is to cast the innovating scientist in the role of an advocate who sets out to propound and defend a certain thesis (p. 110).

    Such an approach to scientific inquiry by no means denies the crucially important role of the standard considerations regarding the nature of scientific evidence. . . .

    Experimentation plays a central role in this probative process.  The devising of experiments to probe a theory at its weakest points, experiments which might—if their eventuation is suitably negative—throw serious doubt upon its claims, comes to be an objective that proponent and opponent share in common.  This is so because counter-indicative experimental findings are a powerful, indeed virtually decisive weapon in the opponent's armory.  And on the other hand, the favorable issue of such an experimental test is a strong asset to the proponent's case (Rescher, 1977, p. 112).

    Such a dialectical-disputational model of the process of scientific inquiry has many attractive features in accounting for the actual phenomenology of scientific work.  Not only does it explain the element of competition that all too plainly characterizes the actual modus operandi of the scientific community.  It accounts also for the "Planck phenomenon" . . . which envisages an old school of stubborn resistance to scientific innovation that is never conquered in the course of progress but simply bypassed (Rescher, 1977, p. 113).

    Billy Koen, an engineer, is one of the few authors of any kind—including historians, philosophers, and social scientists (for others, see Downey, Donovan, and Elliot, 1989)—who has discussed the thinking processes involved in actual engineering practice.  He starts his little book on the subject, Definition of the Engineering Method (1985), with an acknowledgement that almost nothing has been written about engineering method, in contrast to scientific method.

    But a major theme throughout Koen's book, and his final conclusion, is that everyone is an engineer in the sense that he or she must "develop, learn, discover, create and invent the most effective and beneficial heuristics" or problem-solving techniques to deal with life.  Engineers are just very important examples of social problem solvers in a world dominated by technology.

    With respect to engineers, Koen finds that their practice revolves around two things:  heuristic problem solving techniques, and, under this heading, an insistence on using only what is state-of-the-art.  After defining the engineering method in these terms, Koen (1985, p. 41) feels he must take one final step:

    Defining a method does not tell how it is to be used.  We now seek a rule to implement the engineering method.  Since every specific implementation of the engineering method is completely defined by the heuristic it uses, this quest is reduced to finding a heuristic that will tell the individual engineer what to do and when to do it.

    My Rule of Engineering is in every instance to choose the heuristic for use from what my personal [state of the art] takes to be the [state of the art] representing the best engineering practice at the time I am required to choose (Koen, 1985, p. 42).

    Arriving at satisfactory engineering solutions— even the best solutions under the circumstances, given a particular state of the art—does not complete the picture when it comes to technological practice, however.  Too often, as we know sadly enough, technological developments turn out to have unexpected environmental, social, or political consequences.  In order to deal with these in an orderly, and hopefully in an anticipatory fashion, another technique has been developed—technology assessment—to aid in the formulation of technology policy or, more generally, policies for our technological world.  Technology assessment, along with its most common feature, risk/cost/benefit analysis, can be seen as a way of providing decision makers in government or industry with reasonably objective grounds for their decisions.  This is the final arena I want to refer to in which actual practice differs significantly from idealized models.

    Helen Longino, who has recently gained recognition for her novel social approach to scientific knowledge (Longino, 1990), had earlier looked at how a real-life technology assessment works.  The specific case she reviews is the workings of the National Research Council's Committee on Biological Effects of Ionizing Radiation [BEIR], relative to the nuclear generation of electricity.

    Longino (1985, p. 184) concludes that, "The pressure from regulatory and other agencies to have an answer to questions about radiation hazards may force scientists to compromise even in the absence of adequate grounds for consensus."

And she contrasts this with the alleged aims of such commissions to provide objective grounds for decision makers:

    I used to think of this debate as a nice illustration of a view I have developed elsewhere -- that scientific objectivity is a function of the social character of science -- just because the debate is focused on the background or auxiliary assumptions (the risk models) mediating between hypotheses and data.  I am less sanguine about this today.  Certainly the behavior of the National Academy's panel does not meet conditions for objectivity, such as openness to criticism and alternate views.  Not only does it attempt to impose consensus where there clearly is none, but the debate is skewed by the exclusion of points of view such as [John] Gofman's [anti-nuclear views] (Longino, 1985, p. 184).

    What is going on here?  Some people have taken the appearance of these discussions of real-life scientific and technological practice to signal a wholesale rejection of objectivity in science.

    One of the strongest statements of this point of view is to be found in Gayle Ormiston and Raphael Sassower's Narrative Experiments:  The Discursive Authority of Science and Technology (1989).

    Tracing their sources to W.V. Quine, Thomas Kuhn, Paul Feyerabend, Richard Rorty, Michel Foucault, Jacques Derrida, and Jean-Francois Lyotard (along with Ludwig Wittgenstein and John Austin), Ormiston and Sassower (1989, pp. 16-17) say that:

    Instead of locating authority in a particular genre or discursive mode, our identification of discursive displacement attempts to show how the fabrication and deployment of rules is pertinent to any interpretive experiment.  In order to talk about the dissemination of authority, we have used two rules—"use creates" and "all learning is recollection"—, rules legitimated by their use alone.  The use of these rules demonstrates the impossibility of fixing in any permanent fashion the boundaries and limits that constitute cultural matrices.

    The metadisciplinary perspective of this text offers a critically comprehensive overview of the cultural and humanistic context of science and technology.  Such an account is not concerned to provide a hierarchical ordering of science, technology, and the humanities.  Instead, it attempts to undermine any such ordering by demonstrating how science, technology, and the humanities develop in concert with one another; they are mutually constitutive of one another and their culture.  Science, technology, and traditional humanistic studies, then, are modes of one another (Ormiston and Sassower, 1989, p. 14).

    Does this imply relativism?  In a delightful recent attack on relativism, Larry Laudan (1990) constructs a dialogue involving a relativist in debate with a positivist (who makes many references to writings of Carl Hempel), a realist (whose favorite author seems to be Hilary Putnam, in some of his writings), and a pragmatist (Laudan himself).  Laudan (p. xi) says he had to work hard to make his relativist "clever and argumentatively adept," and he notes how two of his chief sources for the position, Quine and Kuhn, try to resist the relativist label.  But Laudan is convinced that all these authors—and he would probably now add Ormiston and Sassower to his list—are relativists, if not explicitly then by implication.

    One recent philosopher who is explicitly relativist—as well as being as clever and argumentatively adept as Laudan could want—is Joseph Margolis in Pragmatism without Foundations:  Reconciling Realism and Relativism (1988).

    Margolis first selects out of all the meanings of relativism one that he can defend.  It is a relativism that rejects foundationalism in all fields, including science, as incompatible with what we know about the limits of human knowing.  With foundationalism also goes transcendentalism, though Margolis is careful to defend the human possibility of deriving certain transcendent truths within historical contexts.

    Enough of that for now, however.  As I said earlier, I do not want to get into the relativism debate here.  My message is a much less ambitious one than that.

    All the talk, among the authors I have quoted, about the discovery process in science (and engineering) reminds me of the work I did in an earlier book, Logic and Scientific Inquiry (Durbin, 1968).  What I focused on there, influenced by Norwood Russell Hanson's Patterns of Discovery (1958; and other writings, e.g., 1961), was a logic of discovery.

    Nonetheless, I ended with a non-logical characterization of the scientific discovery process as a loose set of patterns for the resolution of conflict within research communities that in some ways anticipated the formulations of Ronald Giere, David Hull, and especially Nicholas Rescher.

    I believe I was on the right track in that book (based on my doctoral dissertation) even though my motivation at the time may seem surprising; it was to follow up on leads in Aristotle and the Aristotelian tradition in order to arrive at a better understanding of the processes of modern scientific discovery.

    I would like here to return to the Aristotelian tradition once again for some commonsense hints about how to interpret what the authors quoted here so extensively are saying about the discovery process in science (and engineering).

    In an obscure and seldom-noted passage buried in his systematization of Aristotle's thoughts on art (Summa theologiae I-II, q. 57, art. 3), Thomas Aquinas points out that even deductive reasoning requires art or artfulness or craftiness.  And this is exactly what we have seen George Polya claim, hundreds of years later.  Although Aquinas has many other things to say about art, some of which may be helpful in interpreting technology—even modern technology (see Durbin, 1981)—his treatment of the subject is vague and abstract.

    However, there is another obscure and seldom-noted sentence in Aquinas (in the article cited above) that may suggest how we can be more concrete in interpreting the practice of science (and engineering).  What I have in mind is a passage in which Aquinas equates art with prudence in two areas, ship navigation and military tactics.

    Aquinas is generally quite concrete about what prudence entails [see Summa II-II, qq. 47-56), though I will not here go into all the details.  Among other things, he says that intellectual practice (in any field) requires the obvious:  a good memory, quick wit, good reasoning skills, and so on.  He adds that a lack of good judgment is a fault, along with lack of hard work, precipitation, lack of foresight and circumspection, failure to consult with and learn from the experience of others, and especially changing data and failing to bring the process to a timely conclusion.  (Some of these points are already made in Aristotle's Nicomachean Ethics, book VI, but Aquinas is also systematizing a great many authors, including Cicero.)

    I would not want to be misunderstood here.  In this matter, I am not treating these classical authors as authorities.  They are simply codifying common sense.  It should be obvious to anyone who reflects on any sort of intellectual practice in any intellectual community that such virtues are to be encouraged and such bad habits avoided.  I refer to Aristotle and Aquinas only because I first noticed these commonsense hints in their works; others could just as easily infer them from any of the authors quoted above—from George Polya, fifty years ago, to the most recent sociologists of science doing anthropology-like studies of laboratory life.

    Furthermore, there is an aspect of the matter that is not adequately emphasized by Aristotle and Aquinas.  It is the fact that intellectual activity, especially of a scientific sort, almost always takes place in communities of scientists, or engineers, and other technical workers.  And they have elaborate sets of beliefs, values, procedures, and techniques.

    One of my favorite philosophers, the American Pragmatist George Herbert Mead, is as clear as anyone on what this implies.  Attacking all sorts of individualist epistemologies, from David Hume to Immanuel Kant to G. W. F. Hegel to Bertrand Russell, Mead uses Russell's sense data theory to argue that science could never be built up by the accumulation of individual scientists' experiences.  Instead, these experiences must arise within a world taken for granted by the scientist's community—a world filled with particular meanings, assuming certain laws to be true and to have been arrived at using certain methods, and so on (see Mead, 1964[1917]; see also Kuhn, 1970, pp. 176 and 182-185).

    The conclusion I would draw from all of this is that discovery, the process of discovery in science and other technical fields, is as much a matter of discourse, of intellectual give-and-take, of cooperation (as well as competitiveness), as in other intellectual communities.  There are differences, of course, but I am here focusing on commonalities.

    Furthermore, the communities of scientific and technological researchers exist within broader intellectual communities; for instance, within universities and interrelated professional societies, not to mention publishing houses, the media, and so on.

    In my view, we should take advantage of these commonalities and foster the awareness, not only among students but among administrators of all kinds, public officials, and the public at large that science and technology are above all collaborative enterprises.

    In short, competitiveness surely has a place in science and technology, and it can be easily understood how such competition often gets out of hand.  But a focus on discovering, on the process of discovery, on the day-to-day life of practicing scientists shows how much of the scientific and technological enterprise is a matter of intellectual teamwork -- at its best undertaken out of a sense of social responsibility and for the benefit of humankind.

    When I first wrote that last qualifier (Durbin, 1991), about social responsibility, I was not thinking explicitly about this book and its appeal to philosophers to get involved in activism on technosocial issues.  But the connection between the social-melioration rhetoric of scientists and my appeal here—for philosophers of science to follow scientists' example—seems appropriate in the present context.

    What I have emphasized throughout the book is that philosophers—and perhaps especially my fellow philosophers of technology—ought to profess the same noble goals, of serving humankind, as scientists do.  And this seems to me all the more urgent, for both scientists and philosophers, in our "age of technology," with its myriad social problems.

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