![]() ![]() We used authors' H indexes from the respective year of publication, which are easy to research. ![]() We used IFs from 2006 throughout because this was the earliest year for which all relevant IFs could be obtained. Unambiguous information about authorship and IF was available for 97 articles by 29 board members. Using Google Scholar, we searched for their publications as first authors between 20. Following Jeang, we concentrated on the 45 editorial board members of Retrovirology as of June 2007. In order to test this prediction, we investigated to what extent the citation frequency of an article can be predicted from the H index of the first author and the journal's IF. Therefore, the number of citations of an article should be independent of the pre-eminence (and thus, of the H index) of its author. As a consequence, a counter-intuitive equal-odds rule is at work, whereby an article's probability of becoming a great success is independent of the number of articles of its author. This is because authors who publish the most highly cited publications also publish the highest number of ignored publications. Not because IFs work particularly well – as Jeang correctly noted, citation frequencies vary greatly for articles in the same journal – but because the H index should be completely unsuitable for this specific task. Previous research suggests that the IF may outperform the H index in predicting an article's number of citations, which is often used as a proxy for article quality. How can you decide which ones are worthy of your time when citation frequencies are not yet available? You may infer article quality from an individualized citation metric like the H index of the author (with H being the largest number of publications of an author that have been cited at least H times) alternatively, you may base your inference on a measure of journal quality like its impact factor (IF, which reflects the average citation frequency of articles from a particular journal). Imagine you want to decide which new articles to read outside your narrow field of specialization. But we also contend that "judging a book by its cover" (i) is deeply engrained in human nature, (ii) can be adaptive because outward appearance is often a probabilistic cue to some hidden quality, (iii) and is often without alternative. We agree with Jeang that individual merit is suitably measured by individualized citation metrics, which also predict scientists' future success well. Before the age of personal computers, so Jeang argues, judging an article by the quality of the journal was almost inevitable but as individualized citation statistics have become readily available, it appears outdated to "judge a book by its cover". Recently, Jeang argued forcefully for the use of individualized citation metrics instead of measures of journal quality for evaluation purposes. ![]()
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