Search arXivSearch

arXiv · 2607.05427

Women Enter Too, but Men Persist:The Temporal Structure of Gender Inequality in the Global Citation Elite

Abstract

In this research, I analyze the gender dynamics of the global citation elite using annual top 2% Stanford/Elsevier lists for 2019-2024. My database includes 1.22 million person-year observations (N=1,221,363), which corresponds to 465,707 unique scientists and scholars from more than 150 countries. I move away from static representations of women in the citation elite toward analyses of entry, exit, and permanent membership in the durable core of this elite. The share of women in the annual citation elite increased from 18.39% in 2019 to 20.98% in 2024. However, women are more strongly represented among first-observed entrants than among continuing members, and their share decreases with the persistence of their presence among the citation elite expressed in years: from 22.19% among single-year members to 17.84% among scientists and scholars present in all six annual lists. Women are generally located closer to the lower boundary of the elite in terms of the citation index deciles - and men are closer to top deciles. My logistic regression models estimate a lower probability of women s membership in the durable core of the citation elite (odds ratio estimate OR=0.69). Women are also more weakly represented in the all-career elite than in the annual elite (15.87% vs. 20.98%). I draw conclusions about gender dynamics within the global citation elite and gender inequalities in science more generally.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Marek Kwiek. 2026-07-01. Women Enter Too, but Men Persist:The Temporal Structure of Gender Inequality in the Global Citation Elite. https://arxiv.org/abs/2607.05427

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

FMMD: A multimodal multidisciplinary dataset of open peer reviews from F1000Research

Automated scholarly paper review (ASPR) has entered the coexistence phase with traditional peer review, where artificial intelligence (AI) systems are increasingly incorporated into real-world manuscript evaluation. In parallel, research on automated and AI-assisted peer review has proliferated. Despite this momentum, empirical progress remains constrained by several critical limitations in existing datasets. While reviewers routinely evaluate figures, tables, and complex layouts to assess scientific claims, most existing datasets remain overwhelmingly text-centric. This bias is reinforced by a narrow focus on data from computer science publications. Furthermore, existing datasets rarely preserve precise alignment between review comments and specific manuscript versions, obscuring the iterative relationship between peer review and manuscript evolution. In response, we introduce FMMD, a multimodal and multidisciplinary open peer review dataset curated from F1000Research. The dataset addresses the current limitations by integrating manuscript-level visual and structural data with version-specific reviewer reports and editorial decisions. By explicitly aligning review comments with the exact article version under review, FMMD enables granular analysis of the peer review lifecycle. Importantly, its coverage of F1000Research extends ASPR research beyond its traditional focus on computer science to a diverse range of scientific disciplines. FMMD supports a range of research tasks, including visual-semantic consistency classification, figure-related review comment generation, and editorial decision prediction based on multimodal manuscript inputs, thereby providing a comprehensive empirical resource for developing and evaluating multimodal ASPR systems and advancing peer review research.

cs.DL

GitScholar: A Dataset for Predicting AI Research Impact from GitHub Engagement

With the rapid pace of AI research and the hundreds of daily new publications, staying up-to-date with the latest developments has become increasingly difficult. For researchers, quickly identifying impactful work is essential, yet manually reviewing each new publication is impractical. Automated impact prediction methods help address this challenge, usually by combining various information sources available, such as a paper's content or citation history. In this work, we propose using GitHub engagement as an additional source and demonstrate that it provides both a timely and accurate signal. To this end, we introduce GitScholar, a novel dataset that links GitHub activity from 444,000 repositories to over 558,000 AI arXiv papers. Our experiments show that GitHub reactions improve early prediction precision by up to 12% over a strong academic baseline. Additionally, we find that GitHub signal offers near-complete coverage of high-impact AI papers, and consistently correlates with future academic success. GitScholar is publicly available at https://huggingface.co/datasets/huawei-csl/GitScholar.

cs.DL

The illusion of neutrality in metric-based research evaluation

Venue prestige and citation counts are two widely used, albeit imperfect, signals of research quality. When the two signals conflict, evaluators must decide how much weight to assign each. Yet, it remains unknown how researchers across disciplines trade off these two signals when evaluating research outcome. To fill this gap, we surveyed 869 researchers using paired choices between hypothetical departmental hiring rules that assigned different weights to venue prestige and citation counts, asking which would produce better science. Among 795 respondents whose choices were largely internally consistent, choices placed nearly equal aggregate weight on the two signals. This apparent balance concealed substantial individual heterogeneity: nearly two in five respondents occupied the most venue-heavy or citation-heavy intervals. Moreover, respondents on average chose more citation-heavy rules than they believed their departments used in hiring. By revealing the subjective judgments that arise when research indicators conflict, our findings reinforce calls for greater caution when quantitative indicators are used to evaluate research.

cs.DL