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二十年传染病动力学研究:出版与引用实践中的性别和种族失衡

Twenty years of infectious disease dynamics research: gender and race imbalances in publication and citation practices.

作者信息

Taube Juliana C, Merritt Alexes, Bansal Shweta

机构信息

Department of Biology, Georgetown University, Washington, DC, USA.

出版信息

Proc Biol Sci. 2025 Jul;292(2050):20250960. doi: 10.1098/rspb.2025.0960. Epub 2025 Jul 9.

DOI:10.1098/rspb.2025.0960
PMID:40628489
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12308323/
Abstract

Publication practices accumulate to affect credibility and career advancement. Understanding authorship and citation practices is critical to addressing inequities. While citation bias has been demonstrated in several fields, it remains uncharacterized in infectious disease dynamics (IDD), a quantitative, interdisciplinary domain highly visible during the COVID-19 pandemic. We analyse IDD articles and their bibliographies from 2000 to 2019 using machine-learning algorithms to infer the gender and race/ethnicity of each article's lead and senior authors. We examine authorship and citation patterns by gender and racial group across geographic scales, including characterizing the author composition of each article's bibliography relative to the field. Our analysis reveals persistent gender and race imbalances in IDD research. Man-authored and White-authored publications dominate the field, with little progress in racial diversification of US and UK publications over the last two decades. Woman-authored articles have the most representative citation practices but are undercited, especially when women are senior authors. In the USA and UK, most citations feature White lead and senior authors, even when citing articles have lead or senior authors of colour. These findings underscore the urgent need for more inclusive IDD research practices. We discuss possible mechanisms and solutions to create opportunities for researchers from underrepresented groups.

摘要

出版行为日积月累,会影响可信度和职业发展。了解作者身份认定和引用行为对于解决不公平问题至关重要。虽然在多个领域已证实存在引用偏见,但在传染病动力学(IDD)领域尚未得到描述,该领域是一个定量的跨学科领域,在新冠疫情期间备受关注。我们使用机器学习算法分析2000年至2019年的IDD文章及其参考文献,以推断每篇文章的第一作者和资深作者的性别和种族/民族。我们在不同地理尺度上按性别和种族群体研究作者身份认定和引用模式,包括描述每篇文章参考文献的作者构成相对于该领域的情况。我们的分析揭示了IDD研究中持续存在的性别和种族不平衡。男性作者和白人作者的出版物主导该领域,在过去二十年中,美国和英国出版物的种族多元化进展甚微。女性作者的文章具有最具代表性的引用行为,但引用不足,尤其是当女性是资深作者时。在美国和英国,大多数引用都以白人第一作者和资深作者为特征,即使被引用文章的第一作者或资深作者是有色人种。这些发现强调了迫切需要更具包容性的IDD研究实践。我们讨论了为代表性不足群体的研究人员创造机会的可能机制和解决方案。

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