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统计学期刊之间引文交流的统计建模。

Statistical modelling of citation exchange between statistics journals.

作者信息

Varin Cristiano, Cattelan Manuela, Firth David

机构信息

Università Ca' Foscari Venezia Italy.

Università degli Studi di Padova Italy.

出版信息

J R Stat Soc Ser A Stat Soc. 2016 Jan;179(1):1-63. doi: 10.1111/rssa.12124. Epub 2015 Nov 3.

Abstract

Rankings of scholarly journals based on citation data are often met with scepticism by the scientific community. Part of the scepticism is due to disparity between the common perception of journals' prestige and their ranking based on citation counts. A more serious concern is the inappropriate use of journal rankings to evaluate the scientific influence of researchers. The paper focuses on analysis of the table of cross-citations among a selection of statistics journals. Data are collected from the database published by Thomson Reuters. Our results suggest that modelling the exchange of citations between journals is useful to highlight the most prestigious journals, but also that journal citation data are characterized by considerable heterogeneity, which needs to be properly summarized. Inferential conclusions require care to avoid potential overinterpretation of insignificant differences between journal ratings. Comparison with published ratings of institutions from the UK's research assessment exercise shows strong correlation at aggregate level between assessed research quality and journal citation 'export scores' within the discipline of statistics.

摘要

基于引用数据的学术期刊排名常常遭到科学界的质疑。部分质疑源于期刊声誉的普遍认知与基于引用次数的排名之间存在差异。一个更严重的问题是不恰当地利用期刊排名来评估研究人员的科学影响力。本文着重分析了部分统计学期刊之间的相互引用表。数据取自汤森路透发布的数据库。我们的研究结果表明,对期刊间的引用交流进行建模有助于凸显最具声望的期刊,但期刊引用数据具有相当大的异质性,需要进行恰当的归纳总结。推理结论需谨慎得出,以免对期刊评级间微不足道的差异进行潜在的过度解读。与英国研究评估活动中公布的机构评级进行比较发现,在统计学学科内,评估的研究质量与期刊引用“输出分数”在总体水平上具有很强的相关性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/521a/4755202/5606fe3593ac/RSSA-179-1-g001.jpg

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