Quantifying Citation Diversity: A Clean Bib Tutorial

Shrinidhi Subramaniam

doi:10.1007/s40614-026-00521-4

2026-10-16

PRE

Shrinidhi Subramaniam *

Elizabeth G. E. Kyonka *

DOI：10.1007/s40614-026-00521-4

原文链接

摘要

En 中文

近期在学术写作中纳入引文多样性陈述的做法体现了引文实践向透明化方向的转变。除其他内容外，该陈述包括基于姓名对第一作者和最后作者人口统计特征的分类，最常见的是性别和种族，这些可通过应用程序编程接口（API）进行估算。在本教程中，我们概述如何使用Clean Bib工具为科学和学术出版物中引用的参考文献生成引文多样性陈述。Clean Bib利用GenderAPI和EthnicolrAPI来分类第一和最后作者姓名最可能是男性、女性、白人作者或有色人种作者。尽管该方法存在局限性，即性别和种族被简化为二元变量且排除了其他重要人口统计特征，但它是一个易于获取且可扩展的工具，可用于初步考察作者特征并反思引文实践。

Keyword:

Behavior analysis

Citational justice

Journals

Representation

Scientific publishing

期刊

Perspectives on Behavior Science

IF:

3.8

论文数:

100

被引数:

686

机构

暂无机构信息

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