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Contributorship, Not Authorship: Use CRediT to Indicate Who Did What

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  • Alex O. Holcombe

    (The University of Sydney, Sydney 2006, Australia)

Abstract

Participation in the writing or revising of a manuscript is, according to many journal guidelines, necessary to be listed as an author of the resulting article. This is the traditional concept of authorship. But there are good reasons to shift to a contributorship model, under which it is not necessary to contribute to the writing or revision of a manuscript, and all those who make substantial contributions to a project are credited. Many journals and publishers have already taken steps in this direction, and further adoption will have several benefits. This article makes the case for continuing to move down that path. Use of a contributorship model should improve the ability of universities and funders to identify effective individual researchers and improving their ability to identify the right mix of researchers needed to advance modern science. Other benefits should include facilitating the formation of productive collaborations and the creation of important scientific tools and software. The CRediT (Contributor Roles Taxonomy) taxonomy is a machine-readable standard already incorporated into some journal management systems and it allows incremental transition toward contributorship.

Suggested Citation

  • Alex O. Holcombe, 2019. "Contributorship, Not Authorship: Use CRediT to Indicate Who Did What," Publications, MDPI, vol. 7(3), pages 1-11, July.
  • Handle: RePEc:gam:jpubli:v:7:y:2019:i:3:p:48-:d:245005
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    References listed on IDEAS

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    Cited by:

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    3. Tanmoy Konar, 2021. "Author-Suggested, Weighted Citation Index: A Novel Approach for Determining the Contribution of Individual Researchers," Publications, MDPI, vol. 9(3), pages 1-8, July.
    4. Anne E Thessen & Paul Bogdan & David J Patterson & Theresa M Casey & César Hinojo-Hinojo & Orlando de Lange & Melissa A Haendel, 2021. "From Reductionism to Reintegration: Solving society’s most pressing problems requires building bridges between data types across the life sciences," PLOS Biology, Public Library of Science, vol. 19(3), pages 1-12, March.
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    6. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.

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