  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.
