Hostname: page-component-89b8bd64d-j4x9h Total loading time: 0 Render date: 2026-05-07T19:01:50.388Z Has data issue: false hasContentIssue false
  • English
  • Français

Diversity of contributions is not efficient but is essential for science

Published online by Cambridge University Press:  05 February 2024

Catherine T. Shea Open the ORCID record for Catherine T. Shea [Opens in a new window]  and
Anita Williams Woolley Open the ORCID record for Anita Williams Woolley [Opens in a new window]
Catherine T. Shea
Affiliation:
Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA, USA. ctshea@andrew.cmu.edu awoolley@andrew.cmu.edu https://www.cmu.edu/tepper/faculty-and-research/faculty-by-area/profiles/shea-catherine.html https://scholars.cmu.edu/418-anita-woolley
Anita Williams Woolley*
Affiliation:
Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA, USA. ctshea@andrew.cmu.edu awoolley@andrew.cmu.edu https://www.cmu.edu/tepper/faculty-and-research/faculty-by-area/profiles/shea-catherine.html https://scholars.cmu.edu/418-anita-woolley
*
*Corresponding author.
Get access Rights & Permissions [Opens in a new window]

Abstract

Dominant paradigms in science foster integration of research findings, but at what cost? Forcing convergence requires centralizing decision-making authority, and risks reducing the diversity of methods and contributors, both of which are essential for the breakthrough ideas that advance science.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e57
Check for updates
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Chatman, J. A., & Flynn, F. J. (2005). Full-cycle micro-organizational behavior research. Organization Science, 16(4), 434–447.CrossRefGoogle Scholar
Daft, R. L., & Lewin, A. Y. (1990). Can organization studies begin to break out of the normal science straitjacket? An editorial essay. Organization Science, 1(1), 1–9.CrossRefGoogle Scholar
Fu, R., Aseri, M., Singh, P. V., & Srinivasan, K. (2022). “Un”fair machine learning algorithms. Management Science, 68(6), 4173–4195. https://doi.org/10.1287/mnsc.2021.4065CrossRefGoogle Scholar
Hollenbeck, J. R., Beersma, B., & Schouten, M. E. (2012). Beyond team types and taxonomies: A dimensional scaling conceptualization for team description. Academy of Management Review, 37(1), 82–106.Google Scholar
Huang, J., Gates, A. J., Sinatra, R., & Barabási, A.-L. (2020). Historical comparison of gender inequality in scientific careers across countries and disciplines. Proceedings of the National Academy of Sciences of the United States of America, 117, 4609–4616. https://doi.org/10.1073/pnas.1914221117CrossRefGoogle ScholarPubMed
Kuhn, T. S. (1962). The structure of scientific revolutions. University of Chicago Press.Google Scholar
Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. (2001). A temporally based framework and taxonomy of team processes. Academy of Management Review, 26(3), 356–376.CrossRefGoogle Scholar
National Academies of Sciences, Engineering, and Medicine. (2018). Sexual harassment of women: Climate, culture, and consequences in academic sciences, engineering, and medicine. National Academies Press. https://doi.org/10.17226/24994Google Scholar
Pfeffer, J. (1993). Barriers to the advance of organizational science: Paradigm development as a dependent variable. Academy of Management Review, 18(4), 599–620.CrossRefGoogle Scholar
Shi, F., & Evans, J. (2023). Surprising combinations of research contents and contexts are related to impact and emerge with scientific outsiders from distant disciplines. Nature Communications, 14(1), Article 1. https://doi.org/10.1038/s41467-023-36741-4Google ScholarPubMed