Systemic gendered citation imbalance in computer science: evidence from conferences and journals

Abstract Gender imbalance persists across science, technology, engineering, and mathematics (STEM) fields, including computer science, where it appears in researcher demographics, productivity, recognition, hiring, and career progression. Given computer science’s rapid expansion and global influence, addressing this imbalance is essential for broadening participation and fueling innovation. Although journal-oriented disciplines exhibit consistent gender imbalances in citation practices, it remains unclear whether similar patterns arise in the conference-centric culture of computer science. Here, we systematically investigate gender imbalance in citations of conference and journal papers in computer science. We find that papers with a woman as first or last author receive fewer citations than expected, partly because of homophilic citation tendencies (i.e., authors tend to cite papers that share specific attributes). This imbalance is especially pronounced for conference papers–particularly those published at top-tier venues–relative to journals. Moreover, we find that the prominence of the first or last author and the structure of their local co-authorship networks are potential drivers of these imbalances. By exploring how conference-centric publishing practices can amplify systemic imbalances in computer science, our study offers insights that may inform efforts to foster more equitable representation in academia.

Authors

Institutions

Publication Details

Journal
Scientometrics
Published
2026-03-31
DOI
https://doi.org/10.1007/s11192-026-05595-0
Primary Topic
scientometrics and bibliometrics research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Systemic gendered citation imbalance in computer science: evidence from conferences and journals

George Fletcher, Sohei Tokuno, Yuya Sasaki, Kazuki Nakajima
Scientometrics
scientometrics and bibliometrics research
article

Systemic gendered citation imbalance in computer science: evidence from conferences and journals

George Fletcher, Sohei Tokuno, Yuya Sasaki, Kazuki Nakajima
article en

Abstract

Abstract Gender imbalance persists across science, technology, engineering, and mathematics (STEM) fields, including computer science, where it appears in researcher demographics, productivity, recognition, hiring, and career progression. Given computer science’s rapid expansion and global influence, addressing this imbalance is essential for broadening participation and fueling innovation. Although journal-oriented disciplines exhibit consistent gender imbalances in citation practices, it remains unclear whether similar patterns arise in the conference-centric culture of computer science. Here, we systematically investigate gender imbalance in citations of conference and journal papers in computer science. We find that papers with a woman as first or last author receive fewer citations than expected, partly because of homophilic citation tendencies (i.e., authors tend to cite papers that share specific attributes). This imbalance is especially pronounced for conference papers–particularly those published at top-tier venues–relative to journals. Moreover, we find that the prominence of the first or last author and the structure of their local co-authorship networks are potential drivers of these imbalances. By exploring how conference-centric publishing practices can amplify systemic imbalances in computer science, our study offers insights that may inform efforts to foster more equitable representation in academia.

ScientometricsVol. 131(4)
Tokyo Metropolitan University (JP), Nara Institute of Science and Technology (JP), Eindhoven University of Technology (NL), The University of Osaka (JP)
Tokyo Metropolitan University, University at Buffalo, Japan Society for the Promotion of Science, Japan Science and Technology Agency
Openalex Percentile: Top 38%
scientometrics and bibliometrics research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.