Journal for general philosophy of science = Zeitschrift fur allgemeine Wissenschaftstheorie最新文献

In recent years, many philosophers of science have rejected the "value-free ideal" for science, arguing that non-epistemic values have a legitimate role to play in scientific inquiry. However, this philosophical position raises the question of how to distinguish between legitimate and illegitimate influences of values in science. In this paper, we argue that those seeking to address this "new" demarcation problem can benefit by drawing lessons from the "old" demarcation problem, in which philosophers tried to find a way of distinguishing between science and non-science. Many of those who worked on this problem ultimately found that efforts to provide necessary and sufficient conditions for defining science failed, and most concluded that the best solution to the problem was to characterize scientific hypotheses, theories, and research programs in terms of some common norms. We suggest that those seeking to distinguish between legitimate and illegitimate value influences on science would do well to adopt a similar approach. Rather than attempting to establish necessary and sufficient conditions for identifying appropriate value influences, it will be more fruitful to evaluate scientific activities based on their adherence to a set of epistemic and ethical norms that can be implemented in scientific practice by means of rules, conventions, policies, and procedures.

According to the Feigl-Reichenbach-Salmon-Schurz pragmatic justification of induction, no predictive method is guaranteed or even likely to work for predicting the future; but if anything will work, induction will work-at least when induction is employed at the meta-level of predictive methods in light of their track records. One entertains a priori all manner of esoteric prediction methods, and is said to arrive a posteriori at the conclusion, based on the actual past, that object-level induction is optimal. Schurz's refinements largely solve the notorious short-run problem. A difficulty is noted, however, related to short-run worries but based on localized disagreement about the past, a feature characteristic of real debates (especially early modern) involving induction in intellectual history. Given the evidence about past events, unfiltered by induction, meta-induction might support a partly non-inductive method-especially as judged by proponents of esoteric prediction methods, who presumably believe that their methods have worked. Thus induction is justified meta-inductively in contexts where it was uncontroversial, while not obviously justified in key contexts where it has been disputed. This objection, momentarily sensed by Reichenbach regarding clairvoyance, is borne out by the Stoics' use of meta-induction to justify both science and divination and by ancient Hebrew examples of meta-induction. Schurz's recently introduced criteria for acceptance of testimony play a crucial role in arriving at object-level induction using meta-induction, but one might question them. Given the need for judgment in accepting testimony, it is unclear that the subjectivity of Howson's Bayesian answer to Hume's problem is overcome.

Otto Neurath's empiricist methodology of economics and his contributions to political economy have gained increasing attention in recent years. We connect this research with contemporary debates regarding the epistemological status of thought experiments by reconstructing Neurath's utopias as linchpins of thought experiments. In our three reconstructed examples of different uses of utopias/dystopias in thought experiments we employ a reformulation of Häggqvist's model for thought experiments and we argue that: (1) Our reformulation of Häggqvist's model more adequately complies with many uses of thought experiments, especially with the open-ended discussions of utopias and dystopias in thought experiments. (2) As a strict logical empiricist, Neurath is committed to a strictly empiricist account of thought experiments. John Norton's empiricist argument view can indeed account for the justifications of empirical beliefs and genuine discoveries targeted by scientific utopianism in three distinct (yet connected) ways, all of which Neurath already contemplated: (2.I) Dealing with utopias and thought experiments on a regular basis increases creativity and inventiveness. (2.II) Particular ways of presenting knowledge facilitate scientific discovery and social progress. (2.III) The use of utopias in thought experiments can prompt conceptual change and allow access to new phenomena. We conclude by highlighting that, even though thought experiments support a positive attitude for exploring new social possibilities, Neurath points out that active decisions are unavoidable. The exploration of alternatives and the awareness of a need for decisions in policy discussion avert a technocratic outlook in social science.

Schurz (2019, ch. 4) argues that probabilistic accounts of induction fail. In particular, he criticises probabilistic accounts of induction that appeal to direct inference principles, including subjective Bayesian approaches (e.g., Howson 2000) and objective Bayesian approaches (see, e.g., Williamson 2017). In this paper, I argue that Schurz' preferred direct inference principle, namely Reichenbach's Principle of the Narrowest Reference Class, faces formidable problems in a standard probabilistic setting. Furthermore, the main alternative direct inference principle, Lewis' Principal Principle, is also hard to reconcile with standard probabilism. So, I argue, standard probabilistic approaches cannot appeal to direct inference to explicate the logic of induction. However, I go on to defend a non-standard objective Bayesian account of induction: I argue that this approach can both accommodate direct inference and provide a viable account of the logic of induction. I then defend this account against Schurz' criticisms.

In "Should We Strive to Make Science Bias‑Free? A Philosophical Assessment of the Reproducibility Crisis", I argue that the problem of bias in science, a key factor in the current reproducibility crisis, is worsened if we follow Heather Douglas and Kevin C. Elliott's advice and introduce non-epistemic values into the evidential assessment of scientific hypotheses. In their response to my paper, Douglas and Elliott complain that I misrepresent their views and fall victim to various confusions. In this rebuttal I argue, by means of an examination of their published views, that my initial interpretation of their work is accurate and that, in their hands, science is generally prone to deviations from truth.

There has been a resurgence between two closely related discussions concerning modern science funding policy. The first revolves around the coherence and usefulness of the distinction between basic and applied science and the second concerns whether science should be free to pursue research according to its own internal standards or pursue socially responsible research agendas that are held accountable to moral or political standards. In this paper, I argue that the distinction between basic and applied science, and the concomitant debate about freedom and social responsibility, require revision. I contend that the distinction can only be maintained in cases of urgent science. I go on to elucidate the notion of urgent science using a case study from research of the climate refugee crisis.

Academic publishing is undergoing a highly transformative process, and many established rules and value systems that are in place, such as traditional peer review (TPR) and preprints, are facing unprecedented challenges, including as a result of post-publication peer review. The integrity and validity of the academic literature continue to rely naively on blind trust, while TPR and preprints continue to fail to effectively screen out errors, fraud, and misconduct. Imperfect TPR invariably results in imperfect papers that have passed through varying levels of rigor of screening and validation. If errors or misconduct were not detected during TPR's editorial screening, but are detected at the post-publication stage, an opportunity is created to correct the academic record. Currently, the most common forms of correcting the academic literature are errata, corrigenda, expressions of concern, and retractions or withdrawals. Some additional measures to correct the literature have emerged, including manuscript versioning, amendments, partial retractions and retract and replace. Preprints can also be corrected if their version is updated. This paper discusses the risks, benefits and limitations of these forms of correcting the academic literature.

Supplementary information: The online version contains supplementary material available at 10.1007/s10838-022-09607-4.

Recently, many scientists have become concerned about an excessive number of failures to reproduce statistically significant effects. The situation has become dire enough that the situation has been named the 'reproducibility crisis'. After reviewing the relevant literature to confirm the observation that scientists do indeed view replication as currently problematic, I explain in philosophical terms why the replication of empirical phenomena, such as statistically significant effects, is important for scientific progress. Following that explanation, I examine various diagnoses of the reproducibility crisis, and argue that for the majority of scientists the crisis is due, at least in part, to a form of publication bias. This conclusion sets the stage for an assessment of the view that evidential relations in science are inherently value-laden, a view championed by Heather Douglas and Kevin Elliott. I argue, in response to Douglas and Elliott, and as motivated by the meta-scientific resistance scientists harbour to a publication bias, that if we advocate the value-ladenness of science the result would be a deepening of the reproducibility crisis.

Over time, various thematic classifications have been put forward to organize science into a coherent system of specialized areas of research. From an analysis of the historical evolution of the criteria used to distinguish the sciences from one another, I propose in this paper a quadripartite typology for the different thematic classification systems propounded by scholars throughout the centuries. Basically, I argue that the criteria used to differentiate the sciences have been alternately drawn from their respective subject matters, kinds of knowledge, methods and aims. Then, I show that several reclassifications occurred in the thematic structure of science. Finally, I argue that such changes in the structure of learning displaced the modalities of contact between the objects, knowledge, methods and aims of the various branches of science, with the result of outlining reshaped intellectual territories conducive to the emergence of new areas of research.