Uljana Feest

The last two decades have seen a rising interest in (a) the notion of a scientific phenomenon as distinct from theories and data, and (b) the intricacies of experimentally producing and stabilizing phenomena. This paper develops an analysis of the stabilization of phenomena that integrates two aspects that have largely been treated separately in the literature: one concerns the skills required for empirical work; the other concerns the strategies by which claims about phenomena are validated. I argue that in order to make sense of the process of stabilization, we need to distinguish between two types of phenomena: phenomena as patterns in the data ( surface regularities ) and phenomena as underlying (or hidden ) regularities. I show that the epistemic relationships that data bear to each of these types of phenomena are different: Data patterns are instantiated by individual data, whereas underlying regularities are indicated by individual data, insofar as they instantiate a data pattern. Drawing on an example from memory research, I argue that neither of these two kinds of phenomenon can be stabilized in isolation. I conclude that what is stabilized when phenomena are stabilized is the fit between surface regularities and hidden regularities

The conceptual pair of "Erklären" and "Verstehen" (explanation and understanding) has been an object of philosophical and methodological debates for well over a century. Discussions – to this day – are centered around the question of whether certain objects or issues, such as those dealing with humans or society, require a special approach, different from that of the physical sciences. In the course of such philosophical discussions, we frequently find references to historical predecessors, such as Dilthey’s discussion of the relationship between "Geisteswissenschaft" and "Naturwissenschaft", Windelband’s distinction between nomothetic and idiographic methods, or Weber’s conception of an interpretative sociology. However, these concepts are rarely placed in the historical contexts of their emergence. Nor have the shifting meanings of these terms been analyzed. The present volume considers a variety of intellectual, social, and material factors that contributed to the debate. Far from reducing the debates to their cultural and institutional contexts, however, the volume also offers careful systematic reconstructions of the arguments at hand, thereby enabling the reader to not only appreciate the situatedness of this exciting period of intellectual history, but also to reflect upon the current relevance of the various interpretations of the dichotomy between explanation and understandin

The authors provide an overview of philosophical discussions about the roles of experiment in science. First, they cover two approaches that took shape under the heading of “new experimentalism” in the 1980s and 1990s. One approach was primarily concerned with questions about entity realism, robustness, and epistemological strategies. The other has focused on exploratory experiments and the dynamic processes of experimental research as such, highlighting its iterative nature and drawing out the ways in which such research is grounded in experimental systems, concepts and operational definitions. Second, the authors look at more recent philosophical work on the epistemology of causal inference, in particular highlighting discussions in the philosophy of the behavioral and social sciences, concerning the extrapolation from laboratory contexts to the world.

This paper provides an interpretation of Hans-Jörg Rheinberger’s notions of epistemic things and historical epistemology . I argue that Rheinberger’s approach articulates a unique contribution to current debates about integrated HPS, and I propose some modifications and extensions of this contribution. Drawing on examples from memory research, I show that Rheinberger is right to highlight a particular feature of many objects of empirical research (“epistemic things”)—especially in the contexts of exploratory experimentation—namely our lack of knowledge about them. I argue that this analysis needs to be supplemented with an account of what scientists do know, and in particular, how they are able to attribute rudimentary empirical contours to objects of research. These contours are closely connected to paradigmatic research designs, which in turn are tied to basic methodological rules for the exploration of the purported phenomena. I suggest that we engage with such rules in order to develop our own normative (epistemological) categories, and I tie this proposal to the idea of a methodological naturalism in philosophy of science