Research productivity distributions exhibit heavy tails because it is common for a
few researchers to accumulate the majority of the top publications and their corresponding
citations. Measurements of this productivity are very sensitive to the field being analyzed
and the distribution used. In particular, distributions such as the lognormal distribution
seem to systematically underestimate the productivity of the top researchers. In this
article, we propose the use of a (log)semi-nonparametric distribution (log-SNP) that nests
the lognormal and captures the heavy tail of the productivity distribution through the
introduction of new parameters linked to high-order moments. To compare the results,
we use research performance data on 140,971 researchers who have produced 253,634
publications in 18 fields of knowledge (O’Boyle and Aguinis, 2012) and show how the
log-SNP distribution provides more accurate measures of the performance of the top
researchers in their respective fields of knowledge
