手稿分类以支持出版机会中的偏见分析
Luc Mottin1, Julien Gobeill1,2, Jeevanthi Liyana Pathirana1,2
1SIB Text Mining, Swiss Institute of Bioinformatics, Geneva, Switzerland.
Studies in health technology and informatics
|May 17, 2025
概括
本研究介绍了一种方法来根据研究设计,专业,样本大小和资金对生物医学手稿进行分类. 这种方法旨在通过控制这些因素来识别出版物接受中的性别偏见.
科学领域:
- 生物医学研究的研究.
- 发表偏见的出版物偏见.
- 性别研究是关于性别的研究.
背景情况:
- 研究人员的多样性推动了创新和有影响力的发现.
- 偏见可能会阻碍女性研究人员和多元化研究人员的出版机会.
- 了解和减轻这些偏见对于科学进步至关重要.
研究的目的:
- 开发和评估分类生物医学手稿在四个关键维度的策略.
- 创建一个框架来调查出版物接受中的性别偏见.
- 调整分析以考虑潜在的混因素,如实验设计,医学专业,样本大小和资金来源.
主要方法:
- 56个生物医学手稿的手动注释,由两位专家审稿人进行.
- 为四个手稿维度开发特设分类策略.
- 对模型性能与注释者之间的协议进行评估.
主要成果:
- 提出的分类策略表现出强的表现.
- 准确度分数从实验设计的75%到资金来源的82%.
- 业绩可与专家互注协议相提并论.
结论:
- 开发的分类方法对于分析生物医学手稿是有效的.
- 这种方法为大规模分析MEDLINE的出版偏差提供了基础.
- 解决研究出版中的性别偏见对于一个多元化和公平的科学界至关重要.
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