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)

I examine ramifications of the widespread view that scientific objectivity gives us a permission to trust scientific knowledge claims. According to a widely accepted account of trust and trustworthiness, trust in scientific knowledge claims involves both reliance on the claims and trust in scientists who present the claims, and trustworthiness depends on expertise, honesty, and social responsibility. Given this account, scientific objectivity turns out to be a hybrid concept with both an epistemic and a moral-political dimension. The epistemic dimension tells (...) us when scientific knowledge claims are reliable, and the moral-political dimension tells us when we can trust scientists to be socially responsible. While the former dimension has received a fair amount of attention, the latter is in need of analysis. I examine what it means for scientists to be socially responsible, that is, to follow “sound” moral and social values in different stages of scientific inquiry. Social responsibility is especially important when scientists function as experts in society. Members of the public and policymakers do not want to rely on scientific research shaped by moral and social values they have good reasons to reject. Moreover, social responsibility is important in social research in which moral and social values can legitimately play many roles. I discuss the strengths and weaknesses of different answers to the question of how social scientists can identify appropriate moral and social values to inform their research. I argue that procedural accounts of social responsibility, such as well-ordered science and deliberative polling, have limitations. (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)

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)