  The paper examines extent of bias in the performance rankings of research
organisations when the assessments are based on unsupervised author-name
disambiguation algorithms. It compares the outcomes of a research performance
evaluation exercise of Italian universities using the unsupervised approach by
Caron and van Eck (2014) for derivation of the universities' research staff,
with those of a benchmark using the supervised algorithm of D'Angelo,
Giuffrida, and Abramo (2011), which avails of input data. The methodology
developed could be replicated for comparative analyses in other frameworks of
national or international interest, meaning that practitioners would have a
precise measure of the extent of distortions inherent in any evaluation
exercises using unsupervised algorithms. This could in turn be useful in
informing policy-makers' decisions on whether to invest in building national
research staff databases, instead of settling for the unsupervised approaches
with their measurement biases.
