Ever since the first meeting of the proponents of the emerging Logical Empiricism in 1923, there existed philosophical differences as well as personal rivalries between the groups in Berlin and Vienna, headed by Hans Reichenbach and Moritz Schlick, respectively. Early theoretical tensions between Schlick and Reichenbach were caused by Reichenbach’s Kantian roots, who himself regarded the Vienna Circle as a sort of anti-realist “positivist school”—as he described it in his Experience and Prediction. One result of this divergence was Schlick’s preference (...) of Carnap over Reichenbach for a position at the University of Vienna, and his decision not to serve as a co-editor with Reichenbach for the journal Erkenntnis that they jointly established in 1930. A second split rooted in different views on induction and probability, which culminated in the Hans Reichenbach’s refusal to serve as an invited author on probability within the International Encyclopedia of Unified Science series ed. by Rudolf Carnap, Charles Morris and Otto Neurath from 1938 onwards. In this regard it is remarkable that also Richard von Mises, who was the second leading figure of Logical Empiricism in Turkish exile, criticized the theory of probability put forward by his former Berlin colleague. In this paper I analyse this controversial exchange, drawing on the relevant correspondence and asking whether these philosophical differences were a typical feature of the pluralism inherent in Logical Empiricism in general. (shrink)

In recent decades “inference to the best explanation” has become a fashionable mode of reasoning. It is, however, highly problematic and flawed in ways that the article expounds. Instead, so it is argued, a process of inference from the best systematization of the relevant determinable facts is a far more plausible and promising procedure.

The paper deals with the relation between abduction and inference to the best explanation. A heuristic and a normative interpretation of IBE are distinguished. Besides, two different normative interpretations —those vindicated by I. Niiniluoto and S. Psillos— are discussed. I conclude that, in principle, Aliseda’s theory of abduction fits better with a heuristic account of IBE.

Bayesianism and Inference to the best explanation are two different models of inference. Recently there has been some debate about the possibility of “bayesianizing” IBE. Firstly I explore several alternatives to include explanatory considerations in Bayes’s Theorem. Then I distinguish two different interpretations of prior probabilities: “IBE-Bayesianism” and “frequentist-Bayesianism”. After detailing the content of the latter, I propose a rule for assessing the priors. I also argue that Freq-Bay: endorses a role for explanatory value in the assessment of scientific hypotheses; (...) avoids a purely subjectivist reading of prior probabilities; and fits better than IBE-Bayesianism with two basic facts about science, i.e., the prominent role played by empirical testing and the existence of many scientific theories in the past that failed to fulfil their promises and were subsequently abandoned. (shrink)

What Is Race? Evidence from Scientists is a picture book for schoolchildren published by UNESCO as part of its high-profile campaign on race. The 87-page, oblong, soft-cover booklet contains bold, semi-abstract, pared-down images accompanied by text, devised to make scientific concepts ‘more easily intelligible to the layman’. Produced by UNESCO’s Department of Mass Communication, the picture book represents the organization’s early-postwar confidence in the power of scientific knowledge as a social remedy and diplomatic tool. In keeping with a significant component (...) of the race campaign, What Is Race? presented genetics as the route to an enlightened, scientific, non-prejudiced understanding of race. This article seeks to explain the book’s management, aesthetics and framing in the context of postwar disciplinary and international politics. Viewing UNESCO’s race campaign as a high point for an internationalist ideology of mass education, this article also analyses the visual and literary arguments of What Is Race? and proposes that the enduring image of genetics as technical and neutral knowledge was in part shaped by UNESCO’s efforts to communicate scientific authority to an apparently ‘popular’ audience. (shrink)

This inaugural handbook documents the distinctive research field that utilizes history and philosophy in investigation of theoretical, curricular and pedagogical issues in the teaching of science and mathematics. It is contributed to by 130 researchers from 30 countries; it provides a logically structured, fully referenced guide to the ways in which science and mathematics education is, informed by the history and philosophy of these disciplines, as well as by the philosophy of education more generally. The first handbook to cover the (...) field, it lays down a much-needed marker of progress to date and provides a platform for informed and coherent future analysis and research of the subject. -/- The publication comes at a time of heightened worldwide concern over the standard of science and mathematics education, attended by fierce debate over how best to reform curricula and enliven student engagement in the subjects There is a growing recognition among educators and policy makers that the learning of science must dovetail with learning about science; this handbook is uniquely positioned as a locus for the discussion. -/- The handbook features sections on pedagogical, theoretical, national, and biographical research, setting the literature of each tradition in its historical context. Each chapter engages in an assessment of the strengths and weakness of the research addressed, and suggests potentially fruitful avenues of future research. A key element of the handbook’s broader analytical framework is its identification and examination of unnoticed philosophical assumptions in science and mathematics research. It reminds readers at a crucial juncture that there has been a long and rich tradition of historical and philosophical engagements with science and mathematics teaching, and that lessons can be learnt from these engagements for the resolution of current theoretical, curricular and pedagogical questions that face teachers and administrators. (shrink)

Aliseda’s Abductive Reasoning is focused on the logical problem of abduction. My paper, in contrast, deals with the epistemic problems raised by this sort of inference. I analyze the relation between abduction and inference to the best explanation (IBE). Firstly a heuristic and a normative interpretation of IBE are distinguished. The epistemic problem is particularly pressing for the latter interpretation, since it is devoid of content without specific epistemic criteria for separating acceptable explanations from those which are not. Then I (...) discuss two different normative interpretations of IBE. I. Niiniliuoto favours a “probabilistic-confirmational” translation of explanatory merit while S. Psillos thinks that the insight of IBE is lost in a pure probabilistic format. My conclusion is that Aliseda’s theory of abduction fits better with a heuristic account of IBE. (shrink)

This chapter aims to revisit the notion of argumentation that is currently used in science education. After acknowledging a consolidated tendency of linguistics-based approaches to the study of ‘school scientific argumentation’, the chapter proposes to shift the interest towards an examination of the epistemic aspects of argumentation, i.e. those that derive from its central participation in science as a process and as a product. The premise of the chapter is that the contributions of the philosophy and history of science and (...) of other science studies and metatheoretical perspectives –which are here collectively called ‘HPS’– constitute a fruitful theoretical background to understand scientific arguments and arguing in educational settings. Based on this premise, five possible ‘bridges’ between argumentation and HPS are proposed; such bridges are identified through a ‘theory-directed’ literature review. (shrink)

Bayesianism and Inference to the best explanation are two different models of inference. Recently there has been some debate about the possibility of “bayesianizing” IBE. Firstly I explore several alternatives to include explanatory considerations in Bayes’s Theorem. Then I distinguish two different interpretations of prior probabilities: “IBE-Bayesianism” and “frequentist-Bayesianism”. After detailing the content of the latter, I propose a rule for assessing the priors. I also argue that Freq-Bay: endorses a role for explanatory value in the assessment of scientific hypotheses; (...) avoids a purely subjectivist reading of prior probabilities; and fits better than IBE-Bayesianism with two basic facts about science, i.e., the prominent role played by empirical testing and the existence of many scientific theories in the past that failed to fulfil their promises and were subsequently abandoned. (shrink)