The reward system of science is the priority rule. The first scientist making a new discovery is rewarded with prestige, while second runners get little or nothing. Michael Strevens, following Philip Kitcher, defends this reward system, arguing that it incentivizes an efficient division of cognitive labor. I argue that this assessment depends on strong implicit assumptions about the replicability of findings. I question these assumptions on the basis of metascientific evidence and argue that the priority rule systematically discourages replication. My (...) analysis leads us to qualify Kitcher and Strevens’s contention that a priority-based reward system is normatively desirable for science. (shrink)

Recent breakthroughs in the history and sociology of science have begun to help us to appreciate the vast complexity and intricate character of empirical endeavours in the sciences. The days when philosophers could blandly gesture towards “observation statements” or “falsification,” as if they were some readily understood phenomena or set of procedures, are gone, happily. This does not mean we can merely use the Duhem-Quine thesis as a shibboleth, however: we are now much more sensitive to immense difficulties in establishing (...) viable claims to replication or establishing the absence of selectivity bias, or convincing others of adequacy of experimental controls. Yet, even so, I would still assert that modern science studies, with only a very few exceptions, have ignored the problem of the constitution of error in the context of precision quantitative measurement. While rather broad claims have been made concerning measurement by Hacking, Cartwright, Franklin and others, none goes so far as to examine the actual manipulation of the numerical data. More distressingly, in the areas of economics and economic methodology, we have not even got so far as have the researchers in science studies. This fact alone should provide grounds for scepticism about the claim in Cartwright that philosophy of scientific inference has something to learn from the history of econometrics. (shrink)

Replicability is usually considered to be one of the cornerstones of science; however, the growing recognition of nonreplicable experiments and studies in scientific journals—a phenomenon that has been called ‘replicability crisis’—has spurred a debate on the meaning, function, and significance of replicability in science. Amid this discussion, it has become clear that replicability is not a monolithic concept; what is still controversial is exactly how the distinction between different kinds of replicability should be laid out terminologically and conceptually, and to (...) what extent it bears on the more general debate on the centrality of replicability in science. This paper’s goals are to clarify the different uses of the terms related to replicability and, more importantly, to conceptually specify the kinds of replicability and their respective epistemic functions. (shrink)

The replication or reproducibility crisis in psychological science has renewed attention to philosophical aspects of its methodology. I provide herein a new, functional account of the role of replication in a scientific discipline: to undercut the underdetermination of scientific hypotheses from data, typically by hypotheses that connect data with phenomena. These include hypotheses that concern sampling error, experimental control, and operationalization. How a scientific hypothesis could be underdetermined in one of these ways depends on a scientific discipline’s epistemic goals, theoretical (...) development, material constraints, institutional context, and their interconnections. I illustrate how these apply to the case of psychological science. I then contrast this “bottom-up” account with “top-down” accounts, which assume that the role of replication in a particular science, such as psychology, must follow from a uniform role that it plays in science generally. Aside from avoiding unaddressed problems with top-down accounts, my bottom-up account also better explains the variability of importance of replication of various types across different scientific disciplines. (shrink)