用完美主义指数鉴别有影响力的科学家和大规模生产者
Identification of Influential Scientists vs. Mass Producers by the Perfectionism Index
摘要：
h指数的概念已经被提出来，可以用一个数字轻松地评估研究人员的表现。然而，如果只使用这个数字，我们会丢失关于作者出版物列表中每篇文章的引用分布的重要信息。在这篇文章中，我们研究了作者的引文曲线，我们定义了两个新的领域与此相关的曲线。我们称之为“惩罚区域”，因为它们越大，作者的表现就越受惩罚。我们利用这些领域建立新的指数，即PI和XPI，旨在将研究人员分为两个不同的类别：“影响者”和“大规模生产者”;前一类产生的文章（几乎所有）具有高影响力，后一类产生了大量具有中等影响力或根本没有影响力的文章。使用微软学术服务的数据，我们主要评估PI作为科学计量学研究的有用工具的优点。我们建立了它的有效性分离的科学家成有影响力的和大规模生产者，我们证明了它的鲁棒性对自我引用，其不相关的传统指数。最后，我们应用PI对数据库、网络和多媒体领域的杰出科学家进行排名，展示了该指数在实现其设计目标方面的优势。
英文摘要：
The concept of h-index has been proposed to easily assess a researcher's performance with a single number. However, by using only this number, we lose significant information about the distribution of citations per article in an author's publication list. In this article, we study an author's citation curve and we define two new areas related to this curve. We call these "penalty areas", since the greater they are, the more an author's performance is penalized. We exploit these areas to establish new indices, namely PI and XPI, aiming at categorizing researchers in two distinct categories: "influentials" and "mass producers"; the former category produces articles which are (almost all) with high impact, and the latter category produces a lot of articles with moderate or no impact at all. Using data from Microsoft Academic Service, we evaluate the merits mainly of PI as a useful tool for scientometric studies. We establish its effectiveness into separating the scientists into influentials and mass producers; we demonstrate its robustness against self-citations, and its uncorrelation to traditional indices. Finally, we apply PI to rank prominent scientists in the areas of databases, networks and multimedia, exhibiting the strength of the index in fulfilling its design goal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
