Revisiting gender bias research in bibliometrics: Standardizing methodological variability using Scholarly Data Analysis (SoDA) Cards

2025-01-30

AI Summary

In gender bias research, inconsistent name disambiguation—particularly for Asian names—and nonstandardized gender inference methods hinder result comparability and reproducibility, impeding evidence-based policy formulation. To address this, we propose Scholarly Data Analysis (SoDA) Cards: the first structured metadata template designed to standardize methodological reporting in scholarly gender bias analysis. SoDA Cards systematically specify multi-source gender validation, handling of ungendered names, and cross-cultural name disambiguation. Grounded in metadata modeling, methodological transparency, and reproducibility engineering, the framework provides an evaluation benchmark for over 70 existing studies. By enabling consistent, auditable, and replicable analyses, SoDA Cards advance consensus on best practices for gender bias assessment in academia, thereby enhancing the reliability of scholarly fairness research and its translational impact on equity-oriented policy.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Gender Bias
• Methodological Inconsistencies
• Asian Names Recognition

Innovation

• SoDA (Scholarly Research Data Card)
• Gender Bias Analysis
• Standardization in Academia