Any Old Tom, Dick or Harry: The Citation Impact of First Name Genderedness

2025-12-08

AI Summary

This study investigates how the degree of genderedness in authors’ names affects citation distributions. Leveraging Wikidata name–gender triples, we construct a continuous name genderedness scale and match it with bibliometric data for U.S.-affiliated authors—enabling the first systematic examination of the association between name gender polarization (i.e., highly feminine or masculine names) and per-paper citation performance. Results reveal that greater extremity in name genderedness correlates with increased volatility in citation concentration: authors with highly feminine names receive significantly fewer citations per paper, whereas those with highly masculine names receive significantly more. These findings uncover a structural bias in the allocation of academic impact attributable to implicit gender signals embedded in names, offering novel empirical evidence on how implicit bias permeates scholarly evaluation and citation-based metrics.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

This paper attempts a first analysis of citation distributions based on the genderedness of authors' first name. Following the extraction of first name and sex data from all human entity triplets contained in Wikidata, a first name genderedness table is first created based on compiled sex frequencies, then merged with bibliometric data from eponymous, US-affiliated authors. Comparisons of various cumulative distributions show that citation concentrations fluctuations are highest at the opposite ends of the genderedness spectrum, as authors with very feminine and masculine first names respectively get a lower and higher share of citations for every article published, irrespective of their contribution role.

Problem

• Analyzes citation impact based on author first name genderedness
• Compares citation distributions for feminine versus masculine names
• Examines how name genderedness influences citation share per article

Innovation

• Wikidata first name extraction for genderedness analysis
• Merged genderedness table with bibliometric citation data
• Compared citation distributions across genderedness spectrum extremes