论文标题
作者的隶属关系如何影响预印度引文数量?分析机构和国家一级的引文偏见
How Does Author Affiliation Affect Preprint Citation Count? Analyzing Citation Bias at the Institution and Country Level
论文作者
论文摘要
引用是科学话语的重要方面,对于量化研究人员的科学影响量化很重要。先前的著作观察到,引用不仅是基于纯粹的学术贡献,而且基于非cholarly属性,例如作者的隶属关系或性别。这样,产生引文偏差。然而,现有作品尚未分析有关引用偏见的预印本,尽管它们在现代学术交流中起着越来越重要的作用。在本文中,我们研究了对作者隶属关系是否受引用偏见的影响。我们使用Lorenz曲线和GINI系数来测量机构层面和国家级别的生物Xiv预印本及其发行商版本的引文偏差。这使我们能够减轻混杂因素的影响,并查看与作者隶属关系有关的引文偏见是否对预印记的影响增加。我们观察到与出版商版本相比,预印本的GINI系数一致。因此,我们可以确认存在引用偏见,并且在预印象的情况下它更为严重。随着预印象的增长,基于隶属关系的引文偏见不仅是作者(例如,在决定引用什么),而且对使用引用进行科学影响量化的人们和机构(例如,基于引用计数的资金决定资金的资助机构)。
Citing is an important aspect of scientific discourse and important for quantifying the scientific impact quantification of researchers. Previous works observed that citations are made not only based on the pure scholarly contributions but also based on non-scholarly attributes, such as the affiliation or gender of authors. In this way, citation bias is produced. Existing works, however, have not analyzed preprints with respect to citation bias, although they play an increasingly important role in modern scholarly communication. In this paper, we investigate whether preprints are affected by citation bias with respect to the author affiliation. We measure citation bias for bioRxiv preprints and their publisher versions at the institution level and country level, using the Lorenz curve and Gini coefficient. This allows us to mitigate the effects of confounding factors and see whether or not citation biases related to author affiliation have an increased effect on preprint citations. We observe consistent higher Gini coefficients for preprints than those for publisher versions. Thus, we can confirm that citation bias exists and that it is more severe in case of preprints. As preprints are on the rise, affiliation-based citation bias is, thus, an important topic not only for authors (e.g., when deciding what to cite), but also to people and institutions that use citations for scientific impact quantification (e.g., funding agencies deciding about funding based on citation counts).