Moral trust & scientific collaboration

Modern scientific knowledge is increasingly collaborative. Much analysis in social epistemology models scientists as self-interested agents motivated by external inducements and sanctions. However, less research exists on the epistemic import of scientists’ moral concern for their colleagues. I argue that scientists’ trust in their colleagues’ moral motivations is a key component of the rationality of collaboration. On the prevailing account, trust is a matter of mere reliance on the self-interest of one’s colleagues. That is, scientists merely rely on external compulsion to motivate self-interested colleagues to be trustworthy collaborators. I show that this self-interest account has significant limitations. First, it cannot fully account for trust by relatively powerless scientists. Second, reliance on self-interest can be self-defeating. For each limitation, I show that moral trust can bridge the gap—when members of the scientific community cannot rely on the self-interest of their colleagues, they rationally place trust in the moral motivations of their colleagues. Case studies of mid-twentieth-century industrial laboratories and exploitation of junior scientists show that such moral trust justifies collaboration when mere reliance on the self-interest of colleagues would be irrational. Thus, this paper provides a more complete and realistic account of the rationality of scientific collaboration.     

Evaluating community science

Community science—scientific investigation conducted partly or entirely by non-professional scientists—has many advantages. For example, community science mobilizes large numbers of volunteers who can, at low cost, collect more data than traditional teams of professional scientists. Participation in research can also increase volunteers’ knowledge about and appreciation of science. At the same time, there are worries about the quality of data that community science projects produce. Can the work of non-professionals really deliver trustworthy results? Attempts to answer this question generally compare data collected by volunteers to data collected by professional scientists. When volunteer data is more variable or less accurate than professionally collected data, then the community science project is judged to be inferior to traditional science. I argue that this is not the right standard to use when evaluating community science, because it relies on a false assumption about the aims of science. I show that if we adopt the view that science has diverse aims which are often in tension with one another, then we cannot justify holding community science data to an expert accuracy standard. Instead, we should evaluate the quality of community science data based on its adequacy-for-purpose.     

Collaboration,interdisciplinarity, and the epistemology of contemporary science

Over the last decades, science has grown increasingly collaborative and interdisciplinary and has come to depart in important ways from the classical analyses of the development of science that were developed by historically inclined philosophers of science half a century ago. In this paper, I shall provide a new account of the structure and development of contemporary science based on analyses of, first, cognitive resources and their relations to domains, and second of the distribution of cognitive resources among collaborators and the epistemic dependence that this distribution implies. On this background I shall describe different ideal types of research activities and analyze how they differ. Finally, analyzing values that drive science towards different kinds of research activities, I shall sketch the main mechanisms underlying the perceived tension between disciplines and interdisciplinarity and argue for a redefinition of accountability and quality control for interdisciplinary and collaborative science.     

The experimenters' regress reconsidered: Replication,tacit knowledge,and the dynamics of knowledge generation

This paper revisits the debate between Harry Collins and Allan Franklin, concerning the experimenters' regress. Focusing my attention on a case study from recent psychology (regarding experimental evidence for the existence of a Mozart Effect), I argue that Franklin is right to highlight the role of epistemological strategies in scientific practice, but that his account does not sufficiently appreciate Collins's point about the importance of tacit knowledge in experimental practice. In turn, Collins rightly highlights the epistemic uncertainty (and skepticism) surrounding much experimental research. However, I will argue that his analysis of tacit knowledge fails to elucidate the reasons why scientists often are (and should be) skeptical of other researchers' experimental results. I will present an analysis of tacit knowledge in experimental research that not only answers to this desideratum, but also shows how such skepticism can in fact be a vital enabling factor for the dynamic processes of experimental knowledge generation.     

The methodological defense of realism scrutinized

I revisit an older defense of scientific realism, the methodological defense, a defense developed by both Popper and Feyerabend. The methodological defense of realism concerns the attitude of scientists, not philosophers of science. The methodological defense is as follows: a commitment to realism leads scientists to pursue the truth, which in turn is apt to put them in a better position to get at the truth. In contrast, anti-realists lack the tenacity required to develop a theory to its fullest. As a consequence, they are less likely to get at the truth.My aim is to show that the methodological defense is flawed. I argue that a commitment to realism does not always benefit science, and that there is reason to believe that a research community with both realists and anti-realists in it may be better suited to advancing science. A case study of the Copernican Revolution in astronomy supports this claim.     

The credit incentive to be a maverick

There is a commonly made distinction between two types of scientists: risk-taking, trailblazing mavericks and detail-oriented followers. A number of recent papers have discussed the question what a desirable mixture of mavericks and followers looks like. Answering this question is most useful if a scientific community can be steered toward such a desirable mixture. One attractive route is through credit incentives: manipulating rewards so that reward-seeking scientists are likely to form the desired mixture of their own accord. Here I argue that (even in theory) this idea is less straightforward than it may seem. Interpreting mavericks as scientists who prioritize rewards over speed and risk, I show in a deliberatively simple model that there is a fixed mixture which is not particularly likely to be desirable and which credit incentives cannot alter. I consider a way around this result, but this has some major drawbacks. I conclude that credit incentives are not as promising a way to create a desirable mixture of mavericks and followers as one might have thought.     

A second look at the colors of the dinosaurs

In earlier work, I predicted that we would probably not be able to determine the colors of the dinosaurs. I lost this epistemic bet against science in dramatic fashion when scientists discovered that it is possible to draw inferences about dinosaur coloration based on the microstructure of fossil feathers (Vinther et al., 2008). This paper is an exercise in philosophical error analysis. I examine this episode with two questions in mind. First, does this case lend any support to epistemic optimism about historical science? Second, under what conditions is it rational to make predictions about what questions scientists will or will not be able answer? In reply to the first question, I argue that the recent work on the colors of the dinosaurs matters less to the debate about the epistemology of historical science than it might seem. In reply to the second question, I argue that it is difficult to specify a policy that would rule out the failed bet without also being too conservative.     

The evidential significance of thought experiment in science

The most promising way to regard thought experiment is as a species of experiment, alongside concrete experiment. Of the authors who take this view, many portray thought experiment as possessing evidential significance intrinsically. In contrast, concrete experiment is nowadays most convincingly portrayed as acquiring evidential significance in a particular area of science at a particular time in consequence of the persuasive efforts of scientists. I argue that the claim that thought experiment possesses evidential significance intrinsically is contradicted by the history of science. Thought experiment, like concrete experiment, has evidential significance only where particular assumptions—such as the Galilean doctrine of phenomena—are taken to hold; under alternative premises, in themselves equally defensible, thought experiment is evidentially inert.     

The Garching nuclear egg: Teaching contemporary history beyond the linguistic turn

In my paper I argue for mobilising recent material heritage at universities in teaching history of contemporary science. Getting your hands dirty in the messy worlds of the laboratory and the storage room, and getting entangled with the commemorative practices of scientists and technicians does not belong to the common experiences of students in history and philosophy of science. Despite the recent material turn in cultural studies, students’ engagement with the material world often remains a linguistic exercise, extending at most to an excursion to the sanitised and academically encultured world of the museum exhibit.I contrast this approach by drawing on experiences of taking students to the Atomei, Germany’s oldest research reactor at the Garching campus of the Munich University of Technology. Decommissioned since 2000, the installation and its history are still controlled by scientists. Studying contemporary laboratories and their materiality has so far been the domain of sociologists and ethnographers. I argue for opening these spaces to historians of science and engaging with the ‘unfinished’ material world of contemporary science. Taking the material seriously beyond the linguistic turn and asking students to explore laboratories and other sites of knowledge production challenges existing histories and historiographies. By exploring local university departments and their recent histories through their material heritage, we can observe everyday science and confront scientists and technicians’ cultures with those of historians’. By engaging with recent material heritage as historians and archivists, students can make an important contribution to enhancing the awareness about this heritage, its implications for history writing, as well as its documentation and preservation.     

Overlapping ontologies and Indigenous knowledge. From integration to ontological self-determination

Current controversies about knowledge integration reflect conflicting ideas of what it means to “take Indigenous knowledge seriously”. While there is increased interest in integrating Indigenous and Western scientific knowledge in various disciplines such as anthropology and ethnobiology, integration projects are often accused of recognizing Indigenous knowledge only insofar as it is useful for Western scientists. The aim of this article is to use tools from philosophy of science to develop a model of both successful integration and integration failures. On the one hand, I argue that cross-cultural recognition of property clusters leads to an ontological overlap that makes knowledge integration often epistemically productive and socially useful. On the other hand, I argue that knowledge integration is limited by ontological divergence. Adequate models of Indigenous knowledge will therefore have to take integration failures seriously and I argue that integration efforts need to be complemented by a political notion of ontological self-determination.     

Explanation,understanding, and unrealistic models

How can false models be explanatory? And how can they help us to understand the way the world works? Sometimes scientists have little hope of building models that approximate the world they observe. Even in such cases, I argue, the models they build can have explanatory import. The basic idea is that scientists provide causal explanations of why the regularity entailed by an abstract and idealized model fails to obtain. They do so by relaxing some of its unrealistic assumptions. This method of ‘explanation by relaxation’ captures the explanatory import of some important models in economics. I contrast this method with the accounts that Daniel Hausman and Nancy Cartwright have provided of explanation in economics. Their accounts are unsatisfactory because they require that the economic model regularities obtain, which is rarely the case. I go on to argue that counterfactual regularities play a central role in achieving ‘understanding by relaxation.’ This has a surprising implication for the relation between explanation and understanding: Achieving scientific understanding does not require the ability to explain observed regularities.     

Incommensurabilities in the work of Thomas Kuhn

I distinguish between two ways in which Kuhn employs the concept of incommensurability based on for whom it presents a problem. First, I argue that Kuhn’s early work focuses on the comparison and underdetermination problems scientists encounter during revolutionary periods (actors’ incommensurability) whilst his later work focuses on the translation and interpretation problems analysts face when they engage in the representation of science from earlier periods (analysts’ incommensurability). Secondly, I offer a new interpretation of actors’ incommensurability. I challenge Kuhn’s account of incommensurability which is based on the compartmentalisation of the problems of both underdetermination and non-additivity to revolutionary periods. Through employing a finitist perspective, I demonstrate that in principle these are also problems scientists face during normal science. I argue that the reason why in certain circumstances scientists have little difficulty in concurring over their judgements of scientific findings and claims while in others they disagree needs to be explained sociologically rather than by reference to underdetermination or non-additivity. Thirdly, I claim that disagreements between scientists should not be couched in terms of translation or linguistic problems (aspects of analysts’ incommensurability), but should be understood as arising out of scientists’ differing judgments about how to take scientific inquiry further.     

Defending the selective confirmation strategy

Most scientific realists today in one way or another confine the object of their commitment to certain components of a successful theory and thereby seek to make realism compatible with the history of theory change. Kyle Stanford calls this move by realists the strategy of selective confirmation and raises a challenge against its contemporary, reliable applicability. In this paper, I critically examine Stanford's inductive argument that is based on past scientists' failures to identify the confirmed components of their contemporary theories. I argue that our ability to make such identification should be evaluated based on the performance of the scientific community as a whole rather than that of individual scientists and that Stanford's challenge fails to raise a serious concern because it focuses solely on individual scientists' judgments, which are either made before the scientific community has reached a consensus or about the value of the posit as a locus for further research rather than its confirmed status.     

Pure science and the problem of progress

How should we understand scientific progress? Kuhn famously discussed science as its own internally driven venture, structured by paradigms. He also famously had a problem describing progress in science, as problem-solving ability failed to provide a clear rubric across paradigm change—paradigm changes tossed out problems as well as solving them. I argue here that much of Kuhn’s inability to articulate a clear view of scientific progress stems from his focus on pure science and a neglect of applied science. I trace the history of the distinction between pure and applied science, showing how the distinction came about, the rhetorical uses to which the distinction has been put, and how pure science came to be both more valued by scientists and philosophers. I argue that the distinction between pure and applied science does not stand up to philosophical scrutiny, and that once we relinquish it, we can provide Kuhn with a clear sense of scientific progress. It is not one, though, that will ultimately prove acceptable. For that, societal evaluations of scientific work are needed.     

The bootstrapped artefact: a collectivist account of technological ontology, functions, and normativity

In 2006, this journal addressed the problem of technological artefacts, and through a series of articles aimed at tackling the ‘dual nature of technical artefacts’, posited an understanding of these as constituted by both a structural (physical) and a functional (intentional) component. This attempt to conceptualise artefacts established a series of important questions, concerning such aspects of material technologies as mechanisms, functions, human intentionality, and normativity. However, I believe that in establishing the ‘dual nature’ thesis, the authors within this issue focused too strongly on technological function. By positing function as the analytic axis of the ‘dual nature’ framework, the theorists did not sufficiently problematise what is ultimately a social phenomenon. Here I posit a complementary analytic approach to this problem; namely, I argue that by using the Strong Programme’s performative theory of social institutions, we can better understand the nature of material technologies. Drawing particularly from Martin Kusch’s work, I here argue that by conceptualising artefacts as artificial kinds, we can better examine technological ontology, functions, and normativity. Ultimately, a Strong Programme approach, constructivist and collectivist in nature, offers a useful elaboration upon the important question raised by the ‘dual nature’ theorists.     

Immanent philosophy of X

In this paper I examine the relationship between historians, philosophers and sociologists of science, and indeed scientists themselves. I argue that (i) they co-habit a shared intellectual territory (science and its past); and (ii) they should be able to do so peacefully, and with mutual respect, even if they disagree radically about how to describe the methods and results of science. I then go on to explore some of the challenges to mutually respectful cohabitation between history, philosophy and sociology of science. I conclude by identifying a familiar kind of project in the philosophy of science which seeks to explore the worldview of a particular scientific discipline, and argue that it too has a right to explore the shared territory even though some historians and sociologists may find it methodologically suspect.     

Seepage,objectivity, and climate science

Based on the disproportionate amount of attention paid by climate scientists to the supposed global warming hiatus, it has recently been argued that contrarian discourse has “seeped into” climate science. While I agree that seepage has occurred, its effects remain unclear. This lack of clarity may give the impression that climate science has been compromised in a way that it hasn't—such a conclusion should be defended against. To do this I argue that the effects of seepage should be analyzed in terms of objectivity. I use seven meanings of objectivity to analyze contrarian discourse's impact on climate science. The resulting account supports the important point that climate science has not been compromised in a way that invalidates the conclusions its scientists have drawn, despite the reality of seepage having occurred.     

What are the drivers of induction? Towards a Material Theory+

John Norton's Material Theory of Induction (Norton, 2003, 2005, 2008, forthcoming) has a two-fold, negative and positive, goal. The negative goal is to establish that formal logics of induction fail if they are understood as universally applicable schemas of induction. The positive goal is to establish that it is material facts that enable and justify inductive inferences. I argue in this paper that Norton is more successful with his negative than with his positive ambition. While I do not deny that facts constitute an important type of enabler and justifier of inductions, they are by no means the only type. This paper suggests that there are no less than six other types of background information scientists need and use to fuel and warrant inductions. The discussion of additional enablers and justifiers of inductions will further show there are practically important and intellectually challenging methodological issues Norton's theory prevents us from seeing because it leaves out this or that type of enabler and justifier.     

Values in early-stage climate engineering: The ethical implications of “doing the research”

Calls for research on climate engineering have increased in the last two decades, but there remains widespread agreement that many climate engineering technologies (in particular, forms involving global solar radiation management) present significant ethical risks and require careful governance. However, proponents of research argue, ethical restrictions on climate engineering research should not be imposed in early-stage work like in silico modeling studies. Such studies, it is argued, do not pose risks to the public, and the knowledge gained from them is necessary for assessing the risks and benefits of climate engineering technologies. I argue that this position, which I call the “broad research-first” stance, cannot be maintained in light of the entrance of nonepistemic values in climate modeling. I analyze the roles that can be played by nonepistemic political and ethical values in the design, tuning, and interpretation of climate models. Then, I argue that, in the context of early-stage climate engineering research, the embeddedness of values will lead to value judgments that could harm stakeholder groups or impose researcher values on non-consenting populations. I conclude by calling for more robust reflection on the ethics and governance of early-stage climate engineering research.