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Citations optimal growth path: A tool to analyze sensitivity to citations of h-like indexes
Journal of Informetrics ( IF 3.4 ) Pub Date : 2021-09-15 , DOI: 10.1016/j.joi.2021.101215
Meiqian Chen 1 , Zhaoxia Guo 1 , Yucheng Dong 1 , Francisco Chiclana 2, 3 , Enrique Herrera-Viedma 3, 4
Affiliation  

The h-index is a citation-based metric with extensive applications, and several variants have been developed to complement it. This study formulates the optimal growth path (OGP) models of selected h-like indexes, that is, the h-index, g-index, A-index, R-index, and e-index, and analyzes their OGP-allocated strategies of citations. It is argued that the OGP is a useful tool for analyzing the sensitivity of these h-like indexes to citations. Through simulation experiments with both real and random data, the sensitivity of the selected h-like indexes to citations is compared. Interestingly, it is found that the h-index performs the worst according to the OGP. Further, it is shown that combining the h-index with the A-index decreases the sensitivity to the citations of the h-index. In summary, this study provides new insights into how to evaluate scientific outputs based on h-like indexes.



中文翻译:

引文最优增长路径:分析类 h 指数引文敏感性的工具

^ h -index是引用基于广泛的应用,和几个变种指标已经发展到补充。本研究制定了选定的类h指数(即h指数、g指数、A指数、R指数和e指数)的最优增长路径(OGP)模型,并分析了它们的 OGP 分配策略的引用。有人认为 OGP 是分析这些类似h的索引对引用的敏感性的有用工具。通过对真实数据和随机数据的模拟实验,选定h比较类似引用的索引。有趣的是,根据 OGP发现h指数表现最差。此外,还表明将h- index 与A- index 结合会降低对h- index引用的敏感性。总之,这项研究为如何基于h类指标评估科学成果提供了新的见解。

更新日期:2021-09-15
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