Academics evaluating academics: a methodology to inform the review process on top of open citations

Federica Bologna, Angelo Di Iorio, Silvio Peroni, Francesco Poggi

In the past, several works have investigated ways for combining quantitative and qualitative methods in research assessment exercises. In this work, we aim at introducing a methodology to explore whether citation-based metrics, calculated only considering open bibliographic and citation data, can yield insights on how human peer-review of research assessment exercises is conducted. To understand if and what metrics provide relevant information, we propose to use a series of machine learning models to replicate the decisions of the committees of the research assessment exercises.

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