🤖 AI Summary
This study addresses the excessive polarization observed in Italy’s Institutional Scientific Productivity Index (ISPD) rankings, which stems primarily from the unmodeled homogeneity of normalized scores within academic departments. The work formally characterizes, for the first time, the relationship between such intra-departmental score homogeneity and department size. To mitigate this bias, the authors propose an adjusted ISPD index based on maximum likelihood estimation and introduce a novel Betoidal probability distribution tailored for truncated and rounded publicly available data. Empirical evaluations using real Italian data from 2017 and 2022, complemented by simulation studies, demonstrate that the proposed method substantially alleviates ranking polarization and yields fairer departmental assessments compared to the original ISPD and other existing approaches.
📝 Abstract
In this paper, we consider the academic department ranking system of Italy, which is based on a performance index named Indice Standardizzato di Performance Dipartimentale (ISPD). While critiques to the ISPD have been moved for its marked tendency to polarization, we here formalize a yet unexplored determinant of this phenomenon, that is, the presence of within-department homogeneity among the standardized scores used to build the index. We account for this intra-departmental correlation by modeling it as a function of departments' size. The proposed model, estimated via Maximum Likelihood, allows to build a fairer ranking procedure via the definition of a properly adjusted version of the ISPD. The estimation framework is also adapted to fit publicly available data, which are coarsened by rounding and/or left-truncated. To this end, a novel probability distribution termed Betoidal is introduced. Empirical evidence in favor of the proposed model is found in the 2017 and 2022 data. Moreover, a simulation study shows that the adjusted index significantly overcomes not only the original ISPD, but also other more data-demanding competing proposals.