Gurol Irzik

From its early origins to the present, the development of mainstream economic theory has taken a direction which has excluded the analysis of human needs as a basis for social policy. The problems associated with this orientation are increasingly recognized both by economists and non-economists. As Sen points out, it is indeed strange for a discipline concerned with the well-being of people to neglect the question of needs. Currently, some writers such as Doyal and Gough, post-Keynesian economists such as Lavoie, and those such as Davis and O'Boyle who work in the newly emerging school of social economics have begun to address the question of human needs, especially in relation to problems of policy assessment and evaluation. The approaches of some development economists who have dealt with similar issues were also instrumental in drawing attention to the significance of the long-neglected concept of needs

In this article we develop an account of justice in the distribution of knowledge. We first argue that knowledge is a fundamental interest that grounds claims of justice due to its role in individuals’ deliberations about the common good, their personal good and the pursuit thereof. Second, we identify the epistemic basic structure of a society, namely, the institutions that determine individuals’ opportunities for acquiring knowledge and discuss what justice requires of them. Our main contention is that a systematic lack of opportunity to acquire knowledge one needs as an individual and a citizen because of the way the epistemic basic structure of a society is organized is an injustice. Finally, we discuss how our account relates to John Rawls’s influential theory of justice.

Drawing on the recent revisionary scholarship regarding logical positivism and its relation to the early post-positivism, I display and question the standard historical understanding of the analytical philosophy of science from the late 1920s to the mid-1970s. I then propose an alternative account based on the internal-external distinction. I conclude by showing some advantages of my alternative narrative that does more justice to the logical positivism than the standard understanding and suggest some further lines of research that it opens up

Although there is universal consensus both in the science education literature and in the science standards documents to the effect that students should learn not only the content of science but also its nature, there is little agreement about what that nature is. This led many science educators to adopt what is sometimes called “the consensus view” about the nature of science (NOS), whose goal is to teach students only those characteristics of science on which there is wide consensus. This is an attractive view, but it has some shortcomings and weaknesses. In this article we present and defend an alternative approach based on the notion of family resemblance. We argue that the family resemblance approach is superior to the consensus view in several ways, which we discuss in some detail

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.