Citation benefit – A journal comparison metric based on full citation distributions

In this paper we present a new measure, benefit, which quantifies the probability that a random paper in journal A has more citations than a random paper in journal B (50% means no benefit), and therefore provides an intuitive way to compare journalsu0027 citation capacity. The metric takes into account full citation distributions, thus addressing some of the criticism laid out against Thomson Reuters journal Impact Factor (IF). We calculated the citation benefit for 16,000 journals containing ~2.5 million articles and showed that the citation benefit is a tight, universal function of the ratio of the IFs of the journals being compared. The tightness of the correlation is a consequence of the fact that journals having the same IF have very similar citation distributions. The citation benefit grows slowly as a function of the ratio of their IF values. For example, the citation benefit of one journal over another is more than 90% only if the ratio of their IFs is greater than ~6, while the factor of two difference in IF values translates into a modest citation benefit (~70%). We present a formula to calculate the citation benefit from IFs alone, and implement it in an online calculator.