Philosophers and metrologists have refuted the view that measurement’s epistemic privilege in scientific practice is explained by its theory-neutrality. Rather, they now explicitly appeal to the role that theories play in measurement. I formulate a challenge for this view: scientists sometimes ascribe epistemic privilege to measurements even if they lack a shared theory about their target quantity, which I illustrate through a case study from early geodesy. Drawing on that case, I argue that the epistemic privilege of measurement precedes shared background theory and is better explained by its pre-theoretic function in enabling a distinctive kind of inquiry.

The persistent pervasiveness of small studies in empirical fields is regularly deplored in scientific discussions. Taken individually, higher-powered studies are more likely to be truth-conducive. However, are they also beneficial for the wider performance of truth-seeking communities? We study the impact of sample sizes on collective exploration dynamics under ordinary conditions of resource limitation. We find that large collaborative studies, because they decrease diversity, can have detrimental effects in realistic circumstances that we characterize precisely. We show how limited inertia mechanisms may partially solve this pooling dilemma and discuss our findings briefly in terms of editorial policies.

Mixed methods research - the combination of qualitative and quantitative data within the same research design to strengthen causal inference - is gaining prominence in the social sciences, but its benefits are contested. Social scientists and philosophers have sought to cash out the epistemic rationale of mixed-methods research but none of the available accounts adequately captures the epistemic gains of mixing methods within a single research design. We argue that what matters is variety of evidence, not of data or methods, and that there are distinct epistemic principles grounding the added value of variety of evidence for causal inference.

According to the Matthew effect, scientists who have previously been rewarded are more likely to be rewarded again. Although widely discussed, it remains contentious what explains this effect and whether it's unfair. Using data about neuroscientists, we examine three factors relevant to clarifying these issues: scientists’ fecundity in supervision, H-index and the location where they obtained a PhD. We find a correlation between location and H-index, but no association between fecundity and H-index. This suggests the Matthew effect entrenches status hierarchies in the scientific credit system not because of exploitative supervisors but because of lucky geographical factors.