The present work constitutes an attempt to make explicit those pragmatic norms successfully operating in empirical science. I will first comment on the initial presuppositions of the discussion, in particular, on those concerning the instrumental character of scientific practice and the nature of scientific goals. Then I will depict the moderately naturalistic frame in which, from this approach, the pragmatic norms make sense. Third, I will focus on the specificity of the pragmatic norms, making special emphasis on what I regard (...) as a key idea underlying them, namely, the view, vigorously advocated by classical pragmatists like C. S. Peirce and G. Vailati, that the best test for objectivity is the test of action. Finally, I am going to put forward a tentative list of pragmatic norms that can be abstracted from a careful observation and analysis of scientific practice as provided by current philosophers of experimentation (A. Franklin and F. Steinle among others). The norms will be divided into four classes corresponding to four aspects of science in which they rule, that is, self-correction, prediction, explanation and both experimentation and computation. In the following account, the formulation of those pragmatic norms successfully governing science will be understood as a contribution that scientifically-oriented pragmatism can make to the normative naturalistic project in epistemology. (shrink)

In this paper, we set out to investigate the following question: if science relies heavily on induction, does philosophy of science rely heavily on induction as well? Using data mining and text analysis methods, we study a large corpus of philosophical texts mined from the JSTOR database (n = 14,199) in order to answer this question empirically. If philosophy of science relies heavily on induction, just as science supposedly does, then we would expect to find significantly more inductive arguments than (...) deductive arguments and abductive arguments in the published works of philosophers of science. Using indicator words to classify arguments by type (namely, deductive, inductive, and abductive arguments), we search through our corpus to find patterns of argumentation. Overall, the results of our study suggest that philosophers of science do rely on inductive inference. But induction may not be as foundational to philosophy of science as it is thought to be for science, given that philosophers of science make significantly more deductive arguments than inductive arguments. Interestingly, our results also suggest that philosophers of science do not rely on abductive arguments all that much, even though philosophers of science consider abduction to be a cornerstone of scientific methodology. (shrink)

In recent years, philosophy of science has witnessed a significant increase in attention directed toward the field’s social relevance. This is demonstrated by the formation of societies with related agendas, the organization of research symposia, and an uptick in work on topics of immediate public interest. The collection of papers that follows results from one such event: a 3-day colloquium on the subject of socially engaged philosophy of science (SEPOS) held at the University of Cincinnati in October 2012. In this (...) introduction, we first survey the recent history of philosophy of science’s social involvement (or lack thereof) and contrast this with the much greater social involvement of the sciences themselves. Next, we argue that the field of philosophy of science bears a special responsibility to contribute to public welfare. We then introduce as a term of art “SEPOS” and articulate what we take to be distinctive about social engagement, with reference to the articles in this collection as exemplars. Finally, we survey the current state of social engagement in philosophy of science and suggest some practical steps for individuals and institutions to support this trajectory. (shrink)

Nineteenth-century Harvard faculty and students looked to philosophical ideas about the proper and effective study of nature as the model of rationality to which their religion must conform. As these ideas changed, notions of rationality changed and so did Harvard religion.

The purpose of this study is to gain a better understanding of the role of abstraction and idealization in Galileo’s scientific inquiries into the law of free falling motion, and their importance in the history of science. Because there is no consensus on the use of the terms “abstraction” and “idealization” in the literature, it is necessary to distinguish between them at the outset. This paper will argue for the importance of abstraction and idealization in physics and the theories and (...) laws of physics constructed with abduction from observations and that these theoretical laws of physics should be tested with deduction and induction thorough quasi-idealized entities rather than empirical results in the everyday world. Galileo’s work is linked to thought experiments in natural science. Galileo, using thought experiments based on idealization, persuaded others that what had been proven true for a ball on an inclined plane would be equally true for a ball falling through a vacuum. (shrink)

The topic of this study is to what extent standards of rational inquiry can be rationally criticized and revised. It is argued that it is rational to treat all such standards as open to criticism and revision. Arguments to the effect that we are fallible with regard to all standards of rational inquiry are presented. Standards cannot be ultimately justified and with certainty established either as adequate or as inescapable presuppositions. Apel's attempt to give ultimate justifications of certain moral and (...) logical rules is examined and criticized. Special attention is given to our fallibility with regard to logical inference rules. The idea that certain logical rules cannot be put into question because any critical argument presupposes them is criticized. It has been claimed that there must be some basic standards which are such that they cannot be rationally evaluated and hence are rationally unrevisable. This is called "the unrevisability thesis". Related to this thesis is the normative policy according to which rationality requires that some standards be treated as unrevisable, the unrevisability policy. Two arguments that have been used to defend the unrevisability thesis and policy are examined and criticized. The conclusion is that we are not forced to accept either the thesis or the policy. The negation of the unrevisability policy is the revisability policy, according to which it is rational to treat all standards as open to rational criticism and revision. Objections that have been directed against the revisability policy are discussed and criticized. According to the objections, the revisability policy leads to rationality relativism. These objections are refuted, and it is argued that it is, on the contrary, rational to adopt the revisability policy and treat all standards of rational inquiry as criticizable and revisable. It is proposed that the rational change of standards should be viewed as a bootstrap process. General features of a bootstrap view of rational change of standards are presented, and it is argued that it is impossible to formulate a real theory of bootstrapping. Two models of standard change are presented and discussed: Laudan's reticulated model of scientific rationality and Briskman's bootstrap theory. It is claimed that in spite of defects and limitations, these models contribute to a richer understanding of bootstrapping. The fallibility and revisability of standards of rational inquiry have consequences for how the normativity of rationality should be understood. The book ends with an account of how the rationality of cognitive actions is related to the idea of the adequacy of standards. A distinction between absolute and standard-relative rationality is made, and it is argued that what an inquiring agent rationally ought to do coincides with what it is standard-relatively rational for him to do. It is shown that this view of rationality of inquiry is nevertheless inconsistent with rationality relativism, and that it is compatible with an objectivistic view of rationality. (shrink)

Greater emphasis on ethical issues is needed in science, technology, engineering, and mathematics (STEM) education. The fiction for specific purposes (FSP) approach, using optimistic science fiction texts, offers a way to focus on ethical reflection that capitalizes on role models rather than negative examples. This article discusses the benefits of using FSP in STEM education more broadly, and then explains how using optimistic fictions in particular encourages students to think in ethically constructive ways. Using examples of science fiction texts with (...) hopeful perspectives, example discussion questions are given to model how to help keep students focused on the ethical issues in a text. Sample writing prompts to elicit ethical reflection are also provided as models of how to guide students to contemplate and analyze ethical issues that are important in their field of study. The article concludes that the use of optimistic fictions, framed through the lens of professional ethics guidelines and reinforced through ethical reflection, can help students to have beneficial ethical models. (shrink)

Carnap wrote a continuation of his reply to Kaplan, which would, however, have made that reply, already by far the longest in the book, too long. So he set aside his projected notes for a continuation to serve as the basis for a separate paper, which he never got around to writing. It is transcribed here from his shorthand and translated into English, with some introductory notes to provide a little context.

This paper examines the distinction that is sometimes drawn between analysis and interpretation in the context of qualitative research, and the processes of critical analysis that underpin reflective practice. The authors consider the complementary logical processes involved in analysis and interpretation, and propose a cycle of reductive, inductive and hypothetico-deductive testing that is both rational and creative. The authors argue that the goal of critical reflection and qualitative data analysis is not to produce knowledge that can be justified in terms (...) of ‘correspondence’ to ‘reality’. Instead we propose a pragmatic, coherence view of knowledge that emphasizes the centrality of dialogue with texts, evidence, beliefs and practice. We conclude that evidence and belief are inextricably linked and that combined processes of reductive, inductive and hypothetico-deductive logic need to be used in a transparent manner to establish the credibility of an interpretation. Although there is no clear demarcation between what is found and what is constructed, a commitment to coherence is the basis of a pragmatic theory of knowledge. (shrink)

Recent research shows that research programmes (quantitative, qualitative and mixed) in education are not displaced (as suggested by Kuhn) but rather lead to integration. The objective of this study is to present a rationale for mixed methods (integrative) research programs based on contemporary philosophy of science (Lakatos, Giere, Cartwright, Holton, Laudan). This historical reconstruction of episodes from physical science (spanning a period of almost 300 years, 17 th to 20 th century) does not agree with the positivist image of science. (...) Quantitative data (empirical evidence) by itself, does not facilitate progress (despite widespread belief to the contrary), neither in the physical sciences nor in the social sciences (education) A historical reconstruction shows that both Piaget and Pascual-Leone's research programs in cognitive psychology, follow the Galilean idealisation quite closely, similar to the research programs of Newton, Mendeleev, Einstein, Thomson, Rutherford, Millikan and Perl in the physical sciences. This relationship does not imply that researchers in education have to emulate research in the physical sciences. A major argument in favor of mixed methods (integrative) research programs is that it provides a rationale for hypotheses, theories, guiding assumptions and presuppositions to compete and provide alternatives. Similar to the physical sciences, this proliferation of hypotheses leads to controversies and rivalries, and thus facilitates the decision making process of the scientific community. It is concluded that mixed methods research programs (not paradigms) in education can facilitate the construction of robust strategies, provided we let the problem situation (as studied by practicing researchers) decide the methodology. (shrink)

Is knowledge in the natural sciences discovered or constructed? For objectivists, scientific knowledge is discovered through investigations into a mind-independent, natural world. For constructivists, such knowledge is produced through negotiations among members of a professional guild. I examine the clash between the two positions and propose that scientific knowledge is the concurrent outcome from investigations into a natural world and from consensus reached through negotiations of a professional guild. Specifically, I introduce the general methodological notion, instituting science, which incorporates both (...) the discovery and the construction processes in the generation of scientific knowledge. To that end, I use a case study from the biomedical sciences to illustrate the notion. I conclude with a discussion of how this methodological notion helps to address the debate between objectivists and constructivists over the generation of scientific knowledge, and of how it compares with others who have also attempted to address the debate. (shrink)

What Do the Data Tell Us? Justification of scientific theories is a three-place relation between data, theories, and background knowledge. Though this should be a commonplace, many methodologies in science neglect it. The article will elucidate the significance and function of our background knowledge in epistemic justification and their consequences for different scientific methodologies. It is argued that there is no simple and at the same time acceptable statistical algorithm that justifies a given theory merely on the basis of certain (...) data. And even if we think to know the probability of a theory, that does not decide whether we should accept it or not. (shrink)