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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.

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Abstract

This study examines whether there are some general trends across subject fields regarding the factors affecting the number of citations of articles, focusing especially on those factors that are not directly related to the quality or content of articles (extrinsic factors). For this purpose, from 6 selected subject fields (condensed matter physics, inorganic and nuclear chemistry, electric and electronic engineering, biochemistry and molecular biology, physiology, and gastroenterology), original articles published in the same year were sampled (n?=?230-240 for each field). Then, the citation counts received by the articles in relatively long citation windows (6 and 11 years after publication) were predicted by negative binomial multiple regression (NBMR) analysis for each field. Various article features about author collaboration, cited references, visibility, authors' achievements (measured by past publications and citedness), and publishing journals were considered as the explanatory variables of NBMR. Some generality across the fields was found with regard to the selected predicting factors and the degree of significance of these predictors. The Price index was the strongest predictor of citations, and number of references was the next. The effects of number of authors and authors' achievement measures were rather weak.