前一年的引用分数有力地预测了明年的分数:2021年scopus索引前400种期刊的十年证据

Pub Date : 2023-08-06 DOI:10.5530/jscires.12.2.020
Atul Kumar, Jaiprakash M. Paliwal, Vinaydeep Brar, Mahesh Singh, Prashant R Tambe Patil, S. Raibagkar
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引用次数: 0

摘要

在过去的几年里,CiteScore已经成为衡量期刊表现的一个流行指标。在本文中,我们分析了2011年至2021年scopus索引前400种期刊的CiteScores。分析中出现了一些有趣的观察结果。排名前400的期刊的平均CiteScore从2011年的16.48分增加到2021年的31.83分,翻了一番。与此同时,标准差从2011年的13.53增加到2021年的38.18,几乎增加了两倍。CiteScores也显示出偏度和峰度的显著增加,这意味着一年内期刊的CiteScores发生了重大变化。重要的是,前一年的CiteScores有力地预测了下一年的分数。这在过去的十年中一直被观察到。前后十年的平均Pearson相关系数为0.98。我们还表明,即使是只有基本计算机知识的人也可以很容易地预测CiteScore。研究人员可以根据过去一年的CiteScore预测CiteScore,并更好地决定是否在期刊上发表他们当前的研究,并对其可能的CiteScore有一个想法。这样的预测对出版商、编辑人员、索引服务、大学当局和资助机构都是有用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Previous Year’s Cite Score Strongly Predicts the Next Year’s Score: Ten Years of Evidence for the Top 400 Scopus-indexed Journals of 2021
Over the last few years, CiteScore has emerged as a popular metric to measure the performance of Journals. In this paper, we analyze CiteScores of the top 400 Scopus-indexed journals of 2021 for years from 2011 to 2021. Some interesting observations emerged from the analysis. The average CiteScore of the top 400 journals doubled from 16.48 in 2011 to 31.83 in 2021. At the same time, the standard deviation has almost trebled from 13.53 in 2011 to 38.18 in 2021. The CiteScores also show sizable increases for skewness and kurtosis, implying major variations in the CiteScores of the journals for a year. Importantly, the previous year’s CiteScores strongly predict the next year’s scores. This has been observed consistently for the last ten years. The average Pearson correlation coefficient between the preceding and succeeding years’ CiteScores for the ten years is 0.98. We also show that it is easily possible for even people with just basic knowledge of computers to forecast the CiteScore. Researchers can predict CiteScores based on the past year’s CiteScores and decide better about publishing their current research in a journal with an idea about its likely CiteScore. Such a forecast can be useful to publishers, editorial staff, indexing services, university authorities, and funding agencies.
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