Blinding the initial review of applications for Beckman Young Investigator awards reduces prestige bias, but has no impact on the success rates for different genders.

In studies of gender disparities in academia, increased focus is required on within-group variability and between-group overlap of distributions when interpreting and reporting results.

A preregistered survey experiment spanning six disciplines has found weak evidence of bias in favour of authors from high-status countries and institutions.

Generative artificial intelligence, such as OpenAI’s ChatGPT, can be used to analyze scientific texts with specialized constructions, including peer review reports.

A survey of over 300 applicants for faculty positions reveals that there are many paths to securing a job offer.

Grant applications submitted to the NIH by African-American/Black PIs are less likely to be funded than applications from white PIs, and the NIH must find a solution that eliminates this racial disparity.

Almost a quarter of faculty evaluation documents from US and Canadian universities mention Journal Impact Factor and often imply that it measures research quality.

White principal investigators applying to the National Science Foundation are consistently funded at higher rates than most non-white PIs.

There is a systemic bias in research evaluation, i.e., disciplines with a higher proportion of women have lower evaluations and funding success for all researchers, regardless of gender.