评估算法方法,揭示科学著作中的种族,种族和性别差异
Yimeng Song1, Nabarun Dasgupta1, Michelle L Bell1
1Yimeng Song is with the School of the Environment, Yale University, New Haven, CT. Nabarun Dasgupta is with the Gillings School of Global Public Health, University of North Carolina at Chapel Hill. Michelle L. Bell is with the School of the Environment, Yale University, New Haven, CT, and the School of Health Policy and Management, College of Health Sciences, Korea University, Seoul, Republic of Korea.
American journal of public health
|May 9, 2025
概括
机器学习算法发现了科学出版中的作者差异. 预测白人和男性作者的接受率更高,突出了需要准确的人口统计数据与算法一起.
科学领域:
- 图书统计学 图书统计学
- 科学出版科学出版
- 健康 公平 卫生 公平
背景情况:
- 科学期刊中的作者人口统计数据对于理解代表性至关重要.
- 之前的研究已经强调了科学作者的潜在差异.
- 准确的人口统计数据对于分析和解决不平等问题至关重要.
研究的目的:
- 评估种族/种族和性别预测算法的有效性,分析作者模式.
- 为了比较不同机器学习算法对作者人口统计的预测性能.
- 评估这些算法对识别科学论文提交的差异的影响.
主要方法:
- 分析了从2013年到2022年向美国公共卫生杂志 (AJPH) 提交的17667份手稿.
- 利用机器学习算法从名字中预测作者种族/种族 (亚裔,黑人,西班牙裔,白人) 和性别.
- 基于预测的人口统计数据,比较算法性能和分析手稿接受率.
主要成果:
- 预测的白人作者提交的份额和接受率最高 (21.1%),预测的亚洲作者提交的份额和接受率最低 (14.9%).
- 预测女性的接受率 (17.9%) 比男性 (20.5%) 低,这一趋势在大多数种族/民族群体中观察到.
- 算法发现了类似的差异,但受到固有的偏见和预测不准确性的限制.
结论:
- 机器学习算法可以揭示科学出版中的种族/种族和性别的作者差异.
- 预测 白人和男性作者表现出更高的手稿接受率.
- 建议将算法预测与自我识别的人口数据相结合,以提高差异分析的准确性.
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