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Productivity, prominence, and the effects of academic environment

Author

Listed:
  • Samuel F. Way

    (Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309)

  • Allison C. Morgan

    (Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309)

  • Daniel B. Larremore

    (Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309; BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80309)

  • Aaron Clauset

    (Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309; BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80309; Santa Fe Institute, Santa Fe, NM 87501)

Abstract

Faculty at prestigious institutions produce more scientific papers, receive more citations and scholarly awards, and are typically trained at more-prestigious institutions than faculty with less prestigious appointments. This imbalance is often attributed to a meritocratic system that sorts individuals into more-prestigious positions according to their reputation, past achievements, and potential for future scholarly impact. Here, we investigate the determinants of scholarly productivity and measure their dependence on past training and current work environments. To distinguish the effects of these environments, we apply a matched-pairs experimental design to career and productivity trajectories of 2,453 early-career faculty at all 205 PhD-granting computer science departments in the United States and Canada, who together account for over 200,000 publications and 7.4 million citations. Our results show that the prestige of faculty’s current work environment, not their training environment, drives their future scientific productivity, while current and past locations drive prominence. Furthermore, the characteristics of a work environment are more predictive of faculty productivity and impact than mechanisms representing preferential selection or retention of more-productive scholars by more-prestigious departments. These results identify an environmental mechanism for cumulative advantage, in which an individual’s past successes are “locked in” via placement into a more prestigious environment, which directly facilitates future success. The scientific productivity of early-career faculty is thus driven by where they work, rather than where they trained for their doctorate, indicating a limited role for doctoral prestige in predicting scientific contributions.

Suggested Citation

  • Samuel F. Way & Allison C. Morgan & Daniel B. Larremore & Aaron Clauset, 2019. "Productivity, prominence, and the effects of academic environment," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(22), pages 10729-10733, May.
  • Handle: RePEc:nas:journl:v:116:y:2019:p:10729-10733
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    Citations

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    Cited by:

    1. Alexey Lyutov & Yilmaz Uygun & Marc-Thorsten Hütt, 2021. "Machine learning misclassification of academic publications reveals non-trivial interdependencies of scientific disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1173-1186, February.
    2. Barigozzi, Francesca & Manna, Ester, 2020. "Envy in mission-oriented organisations," Journal of Economic Behavior & Organization, Elsevier, vol. 179(C), pages 395-424.
    3. Sarah W Davies & Hollie M Putnam & Tracy Ainsworth & Julia K Baum & Colleen B Bove & Sarah C Crosby & Isabelle M Côté & Anne Duplouy & Robinson W Fulweiler & Alyssa J Griffin & Torrance C Hanley & Tes, 2021. "Promoting inclusive metrics of success and impact to dismantle a discriminatory reward system in science," PLOS Biology, Public Library of Science, vol. 19(6), pages 1-15, June.
    4. Li Hou & Qiang Wu & Yundong Xie, 2022. "Does early publishing in top journals really predict long-term scientific success in the business field?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6083-6107, November.
    5. Liang, Wenyan & Gu, Jun & Nyland, Chris, 2022. "China's new research evaluation policy: Evidence from economics faculty of Elite Chinese universities," Research Policy, Elsevier, vol. 51(1).
    6. Wumei Du & Zheng Xie & Yiqin Lv, 2021. "Predicting publication productivity for authors: Shallow or deep architecture?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5855-5879, July.
    7. Yu, Xiaoyao & Szymanski, Boleslaw K. & Jia, Tao, 2021. "Become a better you: Correlation between the change of research direction and the change of scientific performance," Journal of Informetrics, Elsevier, vol. 15(3).
    8. Xie, Zheng, 2020. "Predicting publication productivity for researchers: A piecewise Poisson model," Journal of Informetrics, Elsevier, vol. 14(3).

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