用标准化卡片统一学术研究中的性别偏见分析方法。
Revisiting gender bias research in bibliometrics: Standardizing methodological variability using Scholarly Data Analysis (SoDA) Cards
- 提出SoDA卡片框架,规范作者姓名消歧与性别识别流程。
- 回顾70篇论文发现方法差异大,影响研究可比性。
- 适合关注学术公平、数据透明的研究者使用。
学术计量中的性别偏见问题长期存在,尽管已有大量研究探讨其在产出、影响力、致谢和自引等方面的体现。然而,作者姓名消歧与性别识别方法的不一致,严重削弱了研究结果的可靠性与可比性,可能加剧误解并阻碍有效干预。对过去12年70篇相关文献的回顾显示,方法从基于姓名的简单查找到算法化、金标准手段不一,尚未形成明确的最佳实践共识。尤其在亚洲姓名消歧与未标注性别标签处理方面面临挑战,凸显了建立标准化、稳健方法的迫切需求。为此,我们提出开发并实施「学术数据分析(SoDA)卡片」,提供结构化框架,用于记录和报告学术数据研究中的关键方法选择,包括作者姓名消歧与性别识别步骤。通过提升透明度与可复现性,助力研究结果更准确地比较与整合,支持基于证据的政策制定,并实现对学术中性别等社会偏见研究方法的长期追踪。
原文摘要 · Abstract (English)
Gender biases in scholarly metrics remain a persistent concern, despite numerous bibliometric studies exploring their presence and absence across productivity, impact, acknowledgment, and self-citations. However, methodological inconsistencies, particularly in author name disambiguation and gender identification, limit the reliability and comparability of these studies, potentially perpetuating misperceptions and hindering effective interventions. A review of 70 relevant publications over the past 12 years reveals a wide range of approaches, from name-based and manual searches to more algorithmic and gold-standard methods, with no clear consensus on best practices. This variability, compounded by challenges such as accurately disambiguating Asian names and managing unassigned gender labels, underscores the urgent need for standardized and robust methodologies. To address this critical gap, we propose the development and implementation of ``Scholarly Data Analysis (SoDA) Cards." These cards will provide a structured framework for documenting and reporting key methodological choices in scholarly data analysis, including author name disambiguation and gender identification procedures. By promoting transparency and reproducibility, SoDA Cards will facilitate more accurate comparisons and aggregations of research findings, ultimately supporting evidence-informed policymaking and enabling the longitudinal tracking of analytical approaches in the study of gender and other social biases in academia.
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