Gurol Irzik

This paper aims to shed light on an aspect of Kuhn's philosophy of science which has not received much attention, namely, the role of interpretation in science. We argue that in The Structure of Scientific Revolutions Kuhn employs the notion of interpretation in the theoretical sense and claims that scientists routinely interpret observations and data during normal science, but emphatically denies that revolutionary science is interpretive in any way. In his later writings, he starts using it in the hermeneutical sense and holds that it is revolutionary rather than normal science that is hermeneutically interpretive. After we explain the differences and similarities between the two senses, we discuss the sources of this shift and its consequences for his mature philosophy. Understanding the role of interpretation in Kuhn's overall philosophy suggests a new line of research that can shed new light on his influence on the social sciences.

This paper aims to shed light on an aspect of Kuhn’s philosophy of science which has not received much attention, namely, the role of interpretation in science. We argue that in The Structure of Scientific Revolutions Kuhn employs the notion of interpretation in the theoretical sense and claims that scientists routinely interpret observations and data during normal science, but emphatically denies that revolutionary science is interpretive in any way. In his later writings, he starts using it in the hermeneutical sense and holds that it is revolutionary rather than normal science that is hermeneutically interpretive. After we explain the differences and similarities between the two senses, we discuss the sources of this shift and its consequences for his mature philosophy. Understanding the role of interpretation in Kuhn’s overall philosophy suggests a new line of research that can shed new light on his influence on the social sciences.

Building, restoring and maintaining well-placed trust between scientists and the public is a difficult yet crucial social task requiring the successful cooperation of various social actors and institutions. Kitcher’s (Science in a democratic society, Prometheus Books, Amherst, 2011) takes up this challenge in the context of liberal democratic societies by extending his ideal model of “well-ordered science” that he had originally formulated in his (Science, truth, and democracy, Oxford University Press, Oxford, 2001). However, Kitcher nowhere offers an explicit account of what it means for the public to invest epistemic trust in science. Yet in order to understand how his extended model and its implementation in the actual world address the problem of trust as well as to evaluate it critically, an explicit account of epistemic public trust in science needs to be given first. In this article we first present such an account and then scrutinize his project of building public trust in science in light of it. We argue that even though Kitcher’s ideal model and his proposals for its implementation in the real world face a number of problems, they can be addressed with the resources of our account.

Nancy Cartwright’s new bookNature’s Capacities and their Measurement(1989)2attempts to achieve something remarkable: refute Hume’s most crucial theses concerning causation and let in capacities largely through methodological arguments. The two central theses of Hume which Cartwright challenges are:(1)Generic causal facts (e.g., causal laws) are reducible to regularities.(2)Singular causal facts are true in virtue of generic causal facts.Cartwright’s arguments against (1) and (2) are methodological in the sense that she arrives at her conclusions by considering our best methods for studying the relationship between probability and causation. Cartwright rejects (1) on the grounds that if we look at the way in which probabilities are related to causal relations as expressed in her principle CC, or if we carefully study the methodology of causal modelling employed in the social, behavioral, and life sciences, we see that to get certain causal information from probabilities we must presuppose the truth of other generic causal facts.

Humean accounts of law are at the same time accounts of causation. Accordingly, since laws of nature are nothing but contingent cosmic regularities, to be a cause is just to be an instance of such a law. It follows from this view that it is logically impossible that there be causally related events which are not law-governed. Any particular cause-effect pair instantiates some law of nature, where the law is understood as a regularity. The regularity itself may be understood phenomenalistically, that is, as holding between sense impressions, or realistically, that is, as holding between objective events or event-types. These days even empiricists are realists to some extent, so I will eschew the radically empiricist version and assume that when a Humean talks about regularities, he is referring to objective events, states or processes in nature, not to his subjective experiences.

Recent studies on probabilistic causation and statistical explanation (Cartwright 1979; Salmon 1984), I believe, have opened up the possibility of a genuine unification between philosophical approaches and causal modeling (CM) in the social, behavioral and biological sciences (Wright 1934; Blalock 1964; Asher 1976). This unification rests on the statistical tools employed, the principle of common cause, the irreducibility of causation to probability or statistics, and the idea of causal process as a suitable framework for understanding causal relationships. The aim of this paper is to draw attention to these four areas of contact by focusing on the relevant aspects of CM.Causal analysis in the social sciences is based on two fundamental notions: model and method. A causal model is an idealized picture of the causal relationships in the world. Method, on the other hand, refers to certain statistical techniques that are used to evaluate and test a causal model using data which consist of joint observations on the model variables.

We provide an analysis of the public's having warranted epistemic trust in science, that is, the conditions under which the public may be said to have well-placed trust in the scientists as providers of information. We distinguish between basic and enhanced epistemic trust in science and provide necessary conditions for both. We then present the controversy regarding the connection between autism and measles–mumps–rubella vaccination as a case study to illustrate our analysis. The realization of warranted epistemic public trust in science requires various societal conditions, which we briefly introduce in the concluding section.

This article develops an account of distributive epistemic justice in the production of scientific knowledge. We identify four requirements: (a) science should produce the knowledge citizens need in order to reason about the common good, their individual good and pursuit thereof; (b) science should produce the knowledge those serving the public need to pursue justice effectively; (c) science should be organized in such a way that it does not aid the wilful manufacturing of ignorance; and (d) when making decisions about epistemic risks, scientists should make sure that there aren’t social groups or weighty interests that are neglected. After discussing these requirements, we examine the relationship between discriminatory and distributive epistemic injustice in science and argue that they often compound each other.

This Introduction to the Special Issue on “Responsible Research and Innovation” outlines features of the philosophical debate about the concepts involved and summarizes the papers assembled in this issue. The topic of RRI is widely discussed in science studies and has made its way into science policy. This SI is intended to make the contributions of philosophers of science more visible. The philosophically relevant parts of the field concern, among others, the processes of public participation in science and their impact on the knowledge produced, the notion of justified public trust in science, and the idea of research pursued for the common good. Such topics bring social procedures together with epistemic and ethical considerations and thus raise philosophical challenges. RRI is subject to the tension between committing research to creating knowledge in harmony with public expectations, on the one hand, while not complying with public wishful thinking, on the other. RRI embodies a friction between serving people’s aspirations and correcting people’s expectations. This special issue is intended to explore the narrow pathway left between these conflicting demands.

The papers collected in this Synthese special issue are the result of a conference that one of us (ES) casually suggested and the other (GI) organized, which took place at Bo˘gaziçi University in Istanbul, in May 2008, to commemorate the seventieth anniversary of the publication of Experience and Prediction. These papers are historical and philosophical in varying degrees. Reichenbach is now often lumped together with the logical positivists of the Vienna Circle, but his ideas, especially those in Experience and Prediction, were often developed in opposition to positivism. We hope that the essays collected here will be a resource for philosophers who work on the problems that Reichenbach addressed, and also that these essays will be useful to historians who want to develop a deeper understanding of Reichenbach in his historical context.

Building, restoring and maintaining well-placed trust between scientists and the public is a difficult yet crucial social task requiring the successful cooperation of various social actors and institutions. Kitcher’s takes up this challenge in the context of liberal democratic societies by extending his ideal model of “well-ordered science” that he had originally formulated in his. However, Kitcher nowhere offers an explicit account of what it means for the public to invest epistemic trust in science. Yet in order to understand how his extended model and its implementation in the actual world address the problem of trust as well as to evaluate it critically, an explicit account of epistemic public trust in science needs to be given first. In this article we first present such an account and then scrutinize his project of building public trust in science in light of it. We argue that even though Kitcher’s ideal model and his proposals for its implementation in the real world face a number of problems, they can be addressed with the resources of our account.

According to the conventional wisdom, Thomas Kuhn’s Structure of Scientific Revolutions played a major role in the demise of logical empiricism by demolishing its key tenets and replacing them with an alternative picture of science that has virtually nothing in common with them. However, the relationship between Kuhn’s views and LE is not at all as straightforward as this claim suggests. While is undoubtedly correct, the revisionist historiography of LE in the last two-and-a-half decades documented that is far from truth. In this chapter I highlight the major findings of this historiography, focusing on “the Carnap-Kuhn connection”. In section 2 I summarize and evaluate Kuhn’s critique of LE and then present his alternative account in SSR. In section 3 I compare Carnap’s conception of science with that of Kuhn’s, limiting myself to the SSR. In section 4 I expand my comparison by taking into account Kuhn’s later views. In section 5 I briefly mention some of the other logical empiricists’ views that are in company with Kuhn’s. In section 6 I turn to the fundamental differences between Kuhn and Carnap.

Philosophers of science have paid little attention, positive or negative, to Lyotard’s book The postmodern condition, even though it has been popular in other fields. We set out some of the reasons for this neglect. Lyotard thought that sciences could be justified by non-scientific narratives. We show why this is unacceptable, and why many of Lyotard’s characterisations of science are either implausible or are narrowly positivist. One of Lyotard’s themes is that the nature of knowledge has changed and thereby so has society itself. However much of what Lyotard says muddles epistemological matters about the definition of ‘knowledge’ with sociological claims about how information circulates in modern society. We distinguish two kinds of legitimation of science: epistemic and socio-political. In proclaiming ‘incredulity towards metanarratives’ Lyotard has nothing to say about how epistemic and methodological principles are to be justified. He also gives a bad argument as to why there can be no epistemic legitimation, which is based on an act/content confusion, and a confusion between making an agreement and the content of what is agreed to. As for socio-political legitimation, Lyotard’s discussion remains at the abstract level of science as a whole rather than at the level of the particular applications of sciences. Moreover his positive points can be accepted without taking on board any of his postmodernist account of science. Finally we argue that Lyotard’s account of paralogy, which is meant to provide a ‘postmodern’ style of justification, is a failure.Author Keywords: Lyotard; Postmodernism; Science; Knowledge; Legitimation; Philosophy of science.

Fleeing from the Nazi regime, along with many German refugees, Hans Reichenbach came to teach at Istanbul University in 1933, accepting the invitation of the Turkish government and stayed in Istanbul until 1938. While much is known about his work and life in Istanbul, the existing literature relies mostly on his letters and works. In this article I try to shed more light on Reichenbach's scholarly activities and personal life by also taking into account the Turkish sources and the academic context in which Reichenbach taught and worked

The idea of family resemblance, when applied to science, can provide a powerful account of the nature of science (NOS). In this chapter we develop such an account by taking into consideration the consensus on NOS that emerged in the science education literature in the last decade or so. According to the family resemblance approach, the nature of science can be systematically and comprehensively characterised in terms of a number of science categories which exhibit strong similarities and overlaps amongst diverse scientific disciplines. We then discuss the virtues of this approach and make some suggestions as to how one can go about teaching it in the classroom.

Recent philosophical studies of probabilistic causation and statistical explanation have opened up the possibility of unifying philosophical approaches with causal modeling as practiced in the social and biological sciences. This unification rests upon the statistical tools employed, the principle of common cause, the irreducibility of causation to statistics, and the idea of causal process as a suitable framework for understanding causal relationships. These four areas of contact are discussed with emphasis on the relevant aspects of causal modeling.