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Academics evaluating academics: a methodology to inform the review process on top of open citations
Federica Bologna, Angelo Di Iorio, Silvio Peroni, Francesco Poggi
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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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