Women’s strength in science: exploring the influence of female participation on research impact and innovation

Abstract

Prevailing attention centers on the plight of female scientists in modern academia. However, female contributions and potential remain insufficiently recognized. To unravel this veil, we leverage large-scale cross-disciplinary datasets from SciSciNet to portray female participation over the past 20 years and quantify the female effect on research using bibliometric indicators. Female ratio is utilized to gauge gender composition within teams. Through successive modeling including mixed-effect and multivariate regressions, we disentangle the intricate effects of female presence and extent of female participation on research impact and dual innovation metrics. We find a steady rise in female-inclusive teams and per-team female ratios over time, with variations across disciplines and broad categories. We demonstrate an inverted U-shaped relationship between female ratio and citation counts—gender-balanced teams typically garner peak citations, while highly-cited vertices drift toward male-skewed teams in male-majority areas. Increasing female participation yields significant gains in innovation. In the upstream of knowledge flow, as captured by novelty (z-scores), female-skewed teams tend to combine more unconventional knowledge. For the downstream, as encapsulated through disruption, female-skewed teams’ innovation efforts have been recognized by follow-on citations. Notably, the female advantage in innovation becomes more evident in male-dominated fields and intensifies over time. Our study offers insights into the unique academic value and the tremendous scientific contributions of females, providing important visions for institutional and policy reforms.

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Acknowledgements

We received no funding for this work. We would like to express our sincere gratitude to the two anonymous reviewers and the chief editor, Prof. Lin Zhang, for their insightful comments and suggestions. We wish to extend our heartfelt thanks to the KLab director, Prof. James Evans. This work was partially completed using the computing resources of the University of Chicago Research Computing Center, authorized by him. We are also grateful to Luna, the beloved dog of the KLab, for her warm companionship in Chicago. She will always be remembered with deep affection and appreciation.

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