Scholars have debated whether changes in digital environments have led to greater concentration or dispersal of scientific citations, but this debate has paid little attention to how other changes in the publication environment may impact the commonly used measures of inequality. Using Monte Carlo experiments, we demonstrate that a variety of inequality measures—including the Gini coefficient, the Herfindahl-Hirschman index, and the percentage of articles ever cited—are substantially biased downward by increases in the total number of articles and citations. We propose and validate a resampling-based correction for this “marginals bias” and apply this correction to empirical data on scholarly citation distributions using Web of Science data covering four broad scientific fields (health, humanities, mathematics and the computer sciences, and the social sciences) from 1996 to 2014. We find that in each field the bulk of the apparent decline in citation inequality in recent years is an artifact of marginals bias, as are most apparent interfield differences in citation inequality. Researchers using inequality measures to compare citation distributions and other distributions with many cases at or near the zero-bound should interpret these metrics carefully and account for the influence of changing marginals.

中文翻译：

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边际变动对学术引用中不平等程度的影响：偏见和重采样校正

学者们一直在争论数字环境的变化是否导致科学引文的集中或散布，但是这场争论却很少关注出版环境中的其他变化如何影响常用的不平等程度。使用蒙特卡洛实验，我们证明，各种不平等措施（包括基尼系数，赫芬达尔-赫希曼指数和曾经引用的文章的百分比）由于文章总数和引用量的增加而显着下降。我们提出并验证了针对这种“边际偏差”的基于重采样的更正，并将此更正应用于使用涵盖四个广泛科学领域（卫生，人文，数学和计算机科学，和社会科学）从1996年到2014年。我们发现，近年来，在每个领域，引文不平等现象的明显减少都是边际偏差的产物，最明显的场间差异就是引文不平等现象。研究人员使用不平等度量来比较引文分布和其他分布，以及在零界限附近或接近零界限的许多情况下，应仔细解释这些度量并考虑边际变化的影响。