Bias in peer review: a case study.

Peer review is the "gold standard" for evaluating journal and conference papers, research proposals, on-going projects and university departments. However, it is widely believed that current systems are expensive, conservative and prone to various forms of bias. One form of bias identified in the literature is "social bias" linked to the personal attributes of authors and reviewers. To quantify the importance of this form of bias in modern peer review, we analyze three datasets providing information on the attributes of authors and reviewers and review outcomes: one from Frontiers - an open access publishing house with a novel interactive review process, and two from Spanish and international computer science conferences, which use traditional peer review. We use a random intercept model in which review outcome is the dependent variable, author and reviewer attributes are the independent variables and bias is defined by the interaction between author and reviewer attributes. We find no evidence of bias in terms of gender, or the language or prestige of author and reviewer institutions in any of the three datasets, but some weak evidence of regional bias in all three. Reviewer gender and the language and prestige of reviewer institutions appear to have little effect on review outcomes, but author gender, and the characteristics of author institutions have large effects. The methodology used cannot determine whether these are due to objective differences in scientific merit or entrenched biases shared by all reviewers.