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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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Questions of definition and measurement continue to constrain a consensus on the measurement of interdisciplinarity. Using Rao-Stirling (RS) Diversity sometimes produces anomalous results. We argue that these unexpected outcomes can be related to the use of “dual-concept diversity” which combines “variety” and “balance” in the definitions (ex ante). We propose to modify RS Diversity into a new indicator (DIV) which operationalizes “variety,” “balance,” and “disparity” independently and then combines them ex post. “Balance” can be measured using the Gini coefficient. We apply DIV to the aggregated citation patterns of 11,487 journals covered by the Journal Citation Reports 2016 of the Science Citation Index and the Social Sciences Citation Index as an empirical domain and, in more detail, to the citation patterns of 85 journals assigned to the Web-of-Science category “information science & library science” in both the cited and citing directions. We compare the results of the indicators and show that DIV provides improved results in terms of distinguishing between interdisciplinary knowledge integration (citing references) versus knowledge diffusion (cited impact). The new diversity indicator and RS diversity measure different features. A routine for the measurement of the various operationalization of diversity (in any data matrix) is made available online. 相似文献
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[目的/意义] 学科交叉文献发现是进行学科交叉研究的重要前提,从海量的文献中快速、精准地发现领域相关交叉文献有助于研究人员快速地把握领域学科交叉动态,识别领域学科交叉研究热点与前沿。提出基于Rao-Stirling指数的领域学科交叉文献发现方法,并以纳米科学与纳米技术领域为例,探讨该方法的可行性。[方法/过程] 在Web of Science数据库下载纳米科学与纳米技术领域文献,构建期刊缩写-全称-学科类别对照表,利用Python编程构建文献参考文献学科分布矩阵,利用R编程计算每篇文献的Rao-Stirling指数进行文献的学科交叉测度,根据测度结果将纳米科学与纳米技术领域文献按照学科交叉程度分为三个水平,以发现领域学科交叉文献。[结果/结论] 基于Rao-Stirling指数的领域学科交叉文献发现方法可以实现领域文献水平的学科交叉测度,并发现学科交叉文献,且该研究方法也同样可扩展到其他研究领域。 相似文献
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