开放式同行评审与 Altmetrics 相关,但与引文无关:来自《自然-通讯》和《PLoS One》的证据

IF 3.4 2区 管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Informetrics Pub Date : 2024-04-27 DOI:10.1016/j.joi.2024.101540
Xi Cheng , Haoran Wang , Li Tang , Weiyan Jiang , Maotian Zhou , Guoyan Wang
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引用次数: 0

摘要

在科学出版物日益透明、引用动机日趋复杂的背景下,公开同行评审(OPR)的适用性和有效性仍存在争议。本研究利用《自然-通讯》(Nature Communications)和《PloS One》上发表的所有文章的引文和数据集(altmetrics),从公开审稿报告和公开审稿人身份两个维度研究了开放同行评审的影响。分析表明,接受 OPR 的文章在引用率方面没有明显优势,但在 altmetrics 方面得分明显较高。大多数学科的数据差异分布显示,公开审稿报告与非公开审稿报告之间存在统计学意义上的显著差异,这反映了整体趋势。对于 OPR 与 altmetrics 相比对引文影响的差异,我们提出了两种可能的解释。第一种解释与 OPR 和非 OPR 研究之间的质量异质性有关,第二种解释与不同作者在不同社区引用和提及文章有关。本研究的结论对未来的 OPR 实践具有政策意义。
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Open peer review correlates with altmetrics but not with citations: Evidence from Nature Communications and PLoS One

Against the backdrop of increasing transparency in scientific publications and the complexity of citation motivations, the applicability and efficacy of open peer review (OPR) remain controversial. Utilizing a dataset of citations and altmetrics for all articles published in Nature Communications and PloS One, in this study the impact of OPR is investigated from the dimensions of open review reports and open identity reviewers. The analysis reveals articles subjected to OPR have no obvious advantage in citations but a notable higher score in altmetrics. The distribution of data variation across most disciplines, displaying a statistically significant difference between OPR and non-OPR, mirrors the overall trend. Two potential explanations for the disparity in OPR's impact on citations compared to altmetrics are proposed. The first relates to the quality heterogeneity between OPR and non-OPR research, while the second is related to the diverse authors citing and mentioning articles in distinct communities. This study's findings carry policy implications for future OPR practices.

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来源期刊
Journal of Informetrics
Journal of Informetrics Social Sciences-Library and Information Sciences
CiteScore
6.40
自引率
16.20%
发文量
95
期刊介绍: Journal of Informetrics (JOI) publishes rigorous high-quality research on quantitative aspects of information science. The main focus of the journal is on topics in bibliometrics, scientometrics, webometrics, patentometrics, altmetrics and research evaluation. Contributions studying informetric problems using methods from other quantitative fields, such as mathematics, statistics, computer science, economics and econometrics, and network science, are especially encouraged. JOI publishes both theoretical and empirical work. In general, case studies, for instance a bibliometric analysis focusing on a specific research field or a specific country, are not considered suitable for publication in JOI, unless they contain innovative methodological elements.
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