Atul Kumar, Jaiprakash M. Paliwal, Vinaydeep Brar, Mahesh Singh, Prashant R Tambe Patil, S. Raibagkar
{"title":"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","authors":"Atul Kumar, Jaiprakash M. Paliwal, Vinaydeep Brar, Mahesh Singh, Prashant R Tambe Patil, S. Raibagkar","doi":"10.5530/jscires.12.2.020","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"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\",\"authors\":\"Atul Kumar, Jaiprakash M. Paliwal, Vinaydeep Brar, Mahesh Singh, Prashant R Tambe Patil, S. Raibagkar\",\"doi\":\"10.5530/jscires.12.2.020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":0,\"journal\":{\"name\":\"\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0,\"publicationDate\":\"2023-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5530/jscires.12.2.020\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5530/jscires.12.2.020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.