共查询到20条相似文献，搜索用时 62 毫秒
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Manolis Antonoyiannakis 《Journal of Informetrics》2018,12(4):1072-1088
Citation averages, and Impact Factors (IFs) in particular, are sensitive to sample size. Here, we apply the Central Limit Theorem to IFs to understand their scale-dependent behavior. For a journal of n randomly selected papers from a population of all papers, we expect from the Theorem that its IF fluctuates around the population average μ, and spans a range of values proportional to , where σ2 is the variance of the population's citation distribution. The dependence has profound implications for IF rankings: The larger a journal, the narrower the range around μ where its IF lies. IF rankings therefore allocate an unfair advantage to smaller journals in the high IF ranks, and to larger journals in the low IF ranks. As a result, we expect a scale-dependent stratification of journals in IF rankings, whereby small journals occupy the top, middle, and bottom ranks; mid-sized journals occupy the middle ranks; and very large journals have IFs that asymptotically approach μ. We obtain qualitative and quantitative confirmation of these predictions by analyzing (i) the complete set of 166,498 IF & journal-size data pairs in the 1997–2016 Journal Citation Reports of Clarivate Analytics, (ii) the top-cited portion of 276,000 physics papers published in 2014–2015, and (iii) the citation distributions of an arbitrarily sampled list of physics journals. We conclude that the Central Limit Theorem is a good predictor of the IF range of actual journals, while sustained deviations from its predictions are a mark of true, non-random, citation impact. IF rankings are thus misleading unless one compares like-sized journals or adjusts for these effects. We propose the Φ index, a rescaled IF that accounts for size effects, and which can be readily generalized to account also for different citation practices across research fields. Our methodology applies to other citation averages that are used to compare research fields, university departments or countries in various types of rankings. 相似文献
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《Journal of Informetrics》2023,17(3):101425
Rousseau and Mutz argued that the existing researches on diversity measure methods, such as the Rao-Stirling index, DIV, etc., have shortcomings, and urged colleagues to find a better framework for diversity measure. Based on Shannon entropy and entropy of degree vector sum, in this contribution a new diversity measure EDVS (Entropy of Degree Vectors Sum) is proposed, which meets all requirements of variety, balance and disparity, and can directly calculate the value of diversity from the observed sample data without calculating the joint probability distribution of two random variables, or mutual information. The empirical results show that: (1) the ranking of the EDVS measure has a higher Spearman correlation coefficient with DIV and DIV* than with Shannon entropy. (2) The EDVS ranking is more relevant with DIV* than with DIV. (3) The diversity of soft science journals is higher than that of hard science journals, which indicates that the interdisciplinary research of social sciences and humanities is more common than that of hard sciences such as sciences and engineering sciences. (4) Rao-Stirling index and DIV index are more sensitive to sample size. The computational complexity of the Rao-Stirling index and DIV index is , while the computational complexity of the EDVS index is . This provides the feasibility for analyzing high-dimension networks and large data sets. Results of verification on different types of data sets show that EDVS can not only effectively measure the diversity of disciplines in interdisciplinary research, but also effectively measure the diversity of other entities. 相似文献
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The h index is a widely used indicator to quantify an individual's scientific research output. But it has been criticized for its insufficient accuracy—the ability to discriminate reliably between meaningful amounts of research output. As a single measure it cannot capture the complete information on the citation distribution over a scientist's publication list. An extensive data set with bibliometric data on scientists working in the field of molecular biology is taken as an example to introduce two approaches providing additional information to the h index: (1) h2 lower, h2 center, and h2 upper are proposed, which allow quantification of three areas within a scientist's citation distribution: the low impact area (h2 lower), the area captured by the h index (h2 center), and the area of publications with the highest visibility (h2 upper). (2) Given the existence of different areas in the citation distribution, the segmented regression model (sRM) is proposed as a method to statistically estimate the number of papers in a scientist's publication list with the highest visibility. However, such sRM values should be compared across individuals with great care. 相似文献