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Abstract

Studies of the concentration of author productivity based upon counts of papers by individual authors will produce measures that change systematically with sample size. Yoshikane, Kageura, and Tsuji seek a statistical framework which will avoid this scale effect problem. Using the number of authors in a field as an absolute concentration measure, and Gini's index as a relative concentration measure, they describe four literatures form both viewpoints with measures insensitive to one another. Both measures will increase with sample size. They then plot profiles of the two measures on the basis of a Monte-Carlo simulation of 1000 trials for 20 equally spaced intervals and compare the characteristics of the literatures. Using data from conferences hosted by four academic societies between 1992 and 1997, they find a coefficient of loss exceeding 0.15 indicating measures will depend highly on sample size. The simulation shows that a larger sample size leads to lower absolute concentration and higher relative concentration. Comparisons made at the same sample size present quite different results than the original data and allow direct comparison of population characteristics.