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Digital technology helps remove gender bias in academia

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Abstract

Science attempts to be a meritocracy; however, in recent years, there has been increasing evidence for systematic gender bias against women. This bias is present in many metrics commonly used to evaluate scientific productivity, which in turn influences hiring and career success. Here we explore a new metric, the Altmetric Attention Score, and find no evidence of bias  across many major journals (Nature, PNAS, PLOS One, New England Journal of Medicine, Cell, and BioRxiv), with equal attention afforded to articles authored by men and women alike. The exception to this rule is the journal Science, which has marked gender bias against women in 2018, equivalent to a mean of 88 more tweets or 11 more news articles and a median of 20 more tweets or 3 more news articles for male than female first authors. Our findings qualify Altmetric, for many types and disciplines of journals, as a potentially unbiased measure of science communication in academia and suggest that new technologies, such as those on which Altmetric is based, might help to democratize academic evaluation.

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Data availability

All data will be made available as supplemental material.

Code availability

All scripts are available as supplemental files and at Github (https://github.com/bjarnebartlett/AltmetricAnalysis).

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Acknowledgments

We would like to acknowledge the SEED Diversity Grant from UH Manoa for providing support and the KTUH radio station for providing a platform for us to share our research. We thank Navin Ramankutty for early discussion. We thank Stacy Konkiel and the Altmetric data science team for advice on the data. We thank an anonymous reviewer for their comments which helped improve the manuscript.

Funding

SEED Diversity Grant from the University of Hawai’i at Manoa.

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Author notes

  1. Julie Fortin and Bjarne Bartlett have contributed equally to this manuscript.

Authors and Affiliations

  1. Institute for Resources, Environment, and Sustainability, University of British Columbia, Vancouver, BC, V6T 2K9, Canada

    Julie Fortin & Zia Mehrabi

  2. Department of Molecular Biosciences and Bioengineering, University of Hawai’i, 3190 Maile Way, MānoaHonolulu, HI, 96822, USA

    Bjarne Bartlett

  3. Department of Tropical Plant and Soil Sciences, University of Hawai’i, 3190 Maile Way, MānoaHonolulu, HI, 96822, USA

    Michael Kantar

  4. Departments of Botany and Zoology, Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada

    Michelle Tseng

  5. School of Public Policy and Global Affairs, University of British Columbia, Vancouver, BC, V6T 1Z2, Canada

    Julie Fortin & Zia Mehrabi

Authors
  1. Julie Fortin
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  2. Bjarne Bartlett
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Contributions

ZM had the idea. BB, JF, ZM, MK MT designed research; BB and JF cleaned and curated data; BB conducted exploratory analyses, JF genderized the data and conducted the statistical modelling with assistance from ZM; BB, JF, ZM, MK, MT wrote the paper.

Corresponding authors

Correspondence to Michael Kantar or Zia Mehrabi.

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The authors declare that they have no competing interests.

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Fortin, J., Bartlett, B., Kantar, M. et al. Digital technology helps remove gender bias in academia. Scientometrics 126, 4073–4081 (2021). https://doi.org/10.1007/s11192-021-03911-4

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  • DOI: https://doi.org/10.1007/s11192-021-03911-4

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