Using the corpus of JSTOR articles, we investigate the role of gender in collaboration patterns across the scholarly landscape by analyzing gender-based homophily--the tendency for researchers to co-author with individuals of the same gender. For a nuanced analysis of gender homophily, we develop methodology necessitated by the fact that the data comprises heterogeneous sub-disciplines and that not all authorships are exchangeable. In particular, we distinguish three components of gender homophily in collaborations: a structural component that is due to demographics and (...) non-gendered authorship norms of a scholarly community, a compositional component which is driven by varying gender representation across sub-disciplines, and a behavioral component which we define as the remainder of observed homophily after its structural and compositional components have been taken into account. Using minimal modeling assumptions, we measure and test for behavioral homophily. We find that significant behavioral homophily can be detected across the JSTOR corpus and show that this finding is robust to missing gender indicators in our data. In a secondary analysis, we show that the proportion of female representation in a field is positively associated with significant behavioral homophily. (shrink)

Previous research has found that funding disparities are driven by applications’ final impact scores and that only a portion of the black/white funding gap can be explained by bibliometrics and topic choice. Using National Institutes of Health R01 applications for council years 2014–2016, we examine assigned reviewers’ preliminary overall impact and criterion scores to evaluate whether racial disparities in impact scores can be explained by application and applicant characteristics. We hypothesize that differences in commensuration—the process of combining criterion scores into (...) overall impact scores—disadvantage black applicants. Using multilevel models and matching on key variables including career stage, gender, and area of science, we find little evidence for racial disparities emerging in the process of combining preliminary criterion scores into preliminary overall impact scores. Instead, preliminary criterion scores fully account for racial disparities—yet do not explain all of the variability—in preliminary overall impact scores. (shrink)

The White Coats for Black Lives and #ShutDownSTEM movements have galvanised biomedical practitioners and researchers to eliminate institutional and systematic racism, including barriers faced by Black researchers in biomedicine and science, technology, engineering, and mathematics. In our study on Black–White funding gaps for National Institutes of Health Research Project grants, we found that the overall award rate for Black applicants is 55% of that for white applicants. How can systems for allocating research grant funding be made more fair while improving (...) their efficiency? (shrink)

Considerable attention has focused on studying reviewer agreement via inter-rater reliability (IRR) as a way to assess the quality of the peer review process. Inspired by a recent study that reported an IRR of zero in the mock peer review of top-quality grant proposals, we use real data from a complete range of submissions to the National Institutes of Health and to the American Institute of Biological Sciences to bring awareness to two important issues with using IRR for assessing peer (...) review quality. First, we demonstrate that estimating local IRR from subsets of restricted-quality proposals will likely result in zero estimates under many scenarios. In both data sets, we find that zero local IRR estimates are more likely when subsets of top-quality proposals rather than bottom-quality proposals are considered. However, zero estimates from range-restricted data should not be interpreted as indicating arbitrariness in peer review. On the contrary, despite different scoring scales used by the two agencies, when complete ranges of proposals are considered, IRR estimates are above 0.6 which indicates good reviewer agreement. Furthermore, we demonstrate that, with a small number of reviewers per proposal, zero estimates of IRR are possible even when the true value is not zero. (shrink)

We propose a new metric for evaluating the informativeness of a set of ratings from a single rater on a given scale. Such evaluations are of interest when raters rate numerous comparable items on the same scale, as occurs in hiring, college admissions, and peer review. Our exposition takes the context of peer review, which involves univariate and multivariate cardinal ratings. We draw on this context to motivate an information-theoretic measure of the refinement of a set of ratings – entropic (...) refinement – as well as two secondary measures. A mathematical analysis of the three measures reveals that only the first, which captures the information content of the ratings, possesses properties appropriate to a refinement metric. Finally, we analyse refinement in real-world grant-review data, finding evidence that overall merit scores are more refined than criterion scores. (shrink)