Citation-Grounded Historical Phrase Profiles for Reviewer Agreement Forecasting

Forecasting when reviewers will agree or disagree is useful for peer-review management because high-disagreement submissions often require additional discussion, stronger area-chair intervention, or more careful reviewer assignment. Prior computational work has mostly focused on predicting final decisions or review scores from papers and completed reviews, while a separate legal machine learning line introduced citation-propagated phrase scoring to model agreement through "memes" that spread over a citation network. This paper proposes a modest empirical-lite adaptation of that idea to scientific peer review. Each reviewer is represented by a historical profile built only from information available before the target submission, with two complementary views: a citation-grounded phrase genealogy inspired by Verma et al. and a dense semantic profile derived from scientific document encoders. Pairwise reviewer agreement is then predicted from reviewer-reviewer similarity, reviewer-submission affinity, and a small set of chronology-safe metadata features. A public-data proof of concept on open F1000Research reviews from RottenReviews shows that the pipeline is feasible and yields a modest macro-F1 lift over a majority baseline, but ranking discrimination remains near chance and calibration remains weak. The main takeaway is therefore intentionally limited: historical phrase profiles are implementable and worth studying further, but the current public-data proxy does not yet establish strong predictive evidence.