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Updated: Aug 27, 2025
07:50
Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
Published on: April 18, 2025
372
Gendered citation patterns among the scientific elite.
Kristina Lerman1, Yulin Yu2, Fred Morstatter1
1Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292.
Summary
Gender disparities persist in science, with citation networks revealing that a scholar's gender significantly impacts recognition, unlike institutional prestige. This affects women's representation in science.
Area of Science:
- Sociology of Science
- Bibliometrics
- Gender Studies
Background:
- Women are underrepresented in science, particularly in senior roles, hindering mentorship and role models.
- Efforts to improve gender diversity have not fully resolved these disparities.
- The lack of elite women scientists perpetuates cycles of underrepresentation.
Purpose of the Study:
- To investigate gender disparities in scientific recognition using citation networks.
- To determine if gender or institutional prestige is a stronger predictor of recognition patterns.
- To provide evidence on the role of gender in scientific success.
Main Methods:
- Analysis of author citation networks for scholars elected to the National Academy of Sciences.
- Quantitative assessment of peer citation patterns to identify gender-based differences.
- Comparison of citation structures between scholars from high-ranked and low-ranked institutions.
Main Results:
- Significant gender disparities were identified in peer citation patterns.
- These gender-based citation differences were sufficiently distinct to predict a scholar's gender.
- No significant disparities were observed based on institutional prestige or rank.
Conclusions:
- A scholar's gender demonstrably influences recognition and success within the scientific community.
- Citation network analysis is a viable method for detecting gender bias in science.
- Prestige of institution is not as influential as gender in scientific recognition patterns.
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