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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Large-scale language analysis of peer review reports
Ivan Buljan1, Daniel Garcia-Costa2, Francisco Grimaldo2
1Department of Research in Biomedicine and Health, University of Split School of Medicine, Split, Croatia.
Abstract:
Peer review is often criticized for being flawed, subjective and biased, but research into peer review has been hindered by a lack of access to peer review reports. Here we report the results of a study in which text-analysis software was used to determine the linguistic characteristics of 472,449 peer review reports. A range of characteristics (including analytical tone, authenticity, clout, three measures of sentiment, and morality) were studied as a function of reviewer recommendation, area of research, type of peer review and reviewer gender. We found that reviewer recommendation had the biggest impact on the linguistic characteristics of reports, and that area of research, type of peer review and reviewer gender had little or no impact. The lack of influence of research area, type of review or reviewer gender on the linguistic characteristics is a sign of the robustness of peer review.
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