Qualitative researchers sometimes talk about objectivity in relation to qualitative data sets. In this paper, I defend a reconstructed notion of objective qualitative data sets that may serve as a useful and reachable guiding ideal in qualitative data generation. In the first part of the paper, I develop the ideal. According to it, a qualitative data set is objective to the extent that it, in conjunction with true assumptions, possesses a combination of good-making features in virtue of which the data (...) set is suited to serve as evidence base for a satisfying answer to the research question under study. In the second part of the paper, I examine and reject two possible lines of objection to this ideal: One is that it picks out the wrong good-making features. The other is that the very focus on good-making features is misguided: the objectivity of a qualitative data set should instead be seen as a matter of how it was generated or evaluated. (shrink)

This paper written as a dialogue between two interlocutors, Julie and a Student, deals with Understanding and its role in the social sciences. The fictional dialogue takes place in Hannover, Germany, and the interlocutors are exchanging arguments about Verstehen and how it should be conceptualized in the philosophy of the social sciences. A range of different approaches is discussed and a naturalistic strategy emerges as a defensible alternative.

A heated debate surrounds the significance of reproducibility as an indicator for research quality and reliability, with many commentators linking a "crisis of reproducibility" to the rise of fraudulent, careless and unreliable practices of knowledge production. Through the analysis of discourse and practices across research fields, I point out that reproducibility is not only interpreted in different ways, but also serves a variety of epistemic functions depending on the research at hand. Given such variation, I argue that the uncritical pursuit (...) of reproducibility as an overarching epistemic value is misleading and potentially damaging to scientific advancement. Requirements for reproducibility, however they are interpreted, are one of many available means to secure reliable research outcomes. Furthermore, there are cases where the focus on enhancing reproducibility turns out not to foster high-quality research. Scientific communities and Open Science advocates should learn from inferential reasoning from irreproducible data, and promote incentives for all researchers to explicitly and publicly discuss their methodological commitments, the ways in which they learn from mistakes and problems in everyday practice, and the strategies they use to choose which research component of any project needs to be preserved in the long term, and how. (shrink)

Lloyd analyzes every proposed evolutionary explanation of female orgasm and argues that all but one suffers from serious evidential errors. Lloyd attributes these errors to two main biases: androcentrism and adaptationism. This paper begins by arguing that the explanation Lloyd favors—the by-product account—is guilty of the androcentrism which supposedly implicates the other explanations of female orgasm with numerous evidential discrepancies. This suggests that there is another error afflicting orgasm research in addition to the biases Lloyd identities. I attempt to diagnose (...) and characterize this additional error. In short, I think the error is using an imprecise definition of the trait in question. Further, Lloyd takes her analysis to support Longino’s contextual empiricist model of scientific objectivity. I consider what implications this analysis has for contextual empiricism. Finally, I argue that theorizing about female orgasm should be done from a conceptual engineering approach. (shrink)