We performed a citation analysis on the Web of Science publications consisting of more than 63 million articles and 1.45 billion citations on 254 subjects from 1981 to 2020. We proposed the Article's Scientific Prestige (ASP) metric and compared this metric to number of citations (#Cit) and journal grade in measuring the scientific impact of individual articles in the large-scale hierarchical and multi-disciplined citation network. In contrast to #Cit, ASP, that is computed based on the eigenvector centrality, considers both direct and indirect citations, and provides steady-state evaluation cross different disciplines. We found that ASP and #Cit are not aligned for most articles, with a growing mismatch amongst the less cited articles. While both metrics are reliable for evaluating the prestige of articles such as Nobel Prize winning articles, ASP tends to provide more persuasive rankings than #Cit when the articles are not highly cited. The journal grade, that is eventually determined by a few highly cited articles, is unable to properly reflect the scientific impact of individual articles. The number of references and coauthors are less relevant to scientific impact, but subjects do make a difference.
翻译:我们在科学网络出版物上进行了引证分析,从1981年到2020年,共有6 300多万篇文章和14.5亿篇关于254个主题的引用。我们提出了文章科学卓越(ASP)衡量标准,并将这一衡量标准与引用数量(#Cit)和期刊等级进行比较,以衡量大型等级和多纪律引用网络中个别文章的科学影响。与#Cit(ASP)相比,ASP(根据精子中心计算)考虑直接和间接引用,并提供不同学科的稳定状态评价。我们发现,ASP和#Cit(ASP)与大多数文章不匹配,而引用较少的文章之间越来越不匹配。虽然这两个衡量标准对于评价诺贝尔奖获奖文章等文章的声望而言是可靠的,但ASP往往提供比#Cit(当文章没有高引用时)更有说服力的排名。最终由少数高引用的文章决定的期刊等级无法正确反映个别文章的科学影响。我们发现,引用和共同作者的数量与科学影响不相关,但主题却不同。