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Gatekeepers of science—Effects of external reviewers’ attributes on the assessments of fellowship applications

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  • Bornmann, Lutz
  • Daniel, Hans-Dieter

Abstract

The scientific norm of universalism prescribes that external reviewers recommend the allocation of awards to young scientists solely on the basis of their scientific achievement. Since the evaluation of grants utilizes scientists with different personal attributes, it is natural to ask whether the norm of universalism reflects the actual evaluation practice.

Suggested Citation

  • Bornmann, Lutz & Daniel, Hans-Dieter, 2007. "Gatekeepers of science—Effects of external reviewers’ attributes on the assessments of fellowship applications," Journal of Informetrics, Elsevier, vol. 1(1), pages 83-91.
    • Handle: RePEc:eee:infome:v:1:y:2007:i:1:p:83-91
    DOI: 10.1016/j.joi.2006.09.005
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    References listed on IDEAS

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    1. Philip Ball, 2005. "Index aims for fair ranking of scientists," Nature, Nature, vol. 436(7053), pages 900-900, August.
    2. Adrian Mander & David Clayton, 2000. "Hotdeck imputation," Stata Technical Bulletin, StataCorp LP, vol. 9(51).
    3. Lutz Bornmann & Hans-Dieter Daniel, 2005. "Selection of research fellowship recipients by committee peer review. Reliability, fairness and predictive validity of Board of Trustees' decisions," Scientometrics, Springer;Akadémiai Kiadó, vol. 63(2), pages 297-320, April.
    4. Lutz Bornmann & Hans-Dieter Daniel, 2005. "Does the h-index for ranking of scientists really work?," Scientometrics, Springer;Akadémiai Kiadó, vol. 65(3), pages 391-392, December.
    5. Lutz Bornmann & Hans-Dieter Daniel, 2005. "Committee peer review at an international research foundation: predictive validity and fairness of selection decisions on post-graduate fellowship applications," Research Evaluation, Oxford University Press, vol. 14(1), pages 15-20, April.
    6. J. Scott Long & Jeremy Freese, 2006. "Regression Models for Categorical Dependent Variables using Stata, 2nd Edition," Stata Press books, StataCorp LP, edition 2, number long2, March.
    7. Lutz Bornmann & Hans-Dieter Daniel, 2006. "Selecting scientific excellence through committee peer review - A citation analysis of publications previously published to approval or rejection of post-doctoral research fellowship applicants," Scientometrics, Springer;Akadémiai Kiadó, vol. 68(3), pages 427-440, September.
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    Citations

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    Cited by:

    1. Hendy Abdoul & Christophe Perrey & Philippe Amiel & Florence Tubach & Serge Gottot & Isabelle Durand-Zaleski & Corinne Alberti, 2012. "Peer Review of Grant Applications: Criteria Used and Qualitative Study of Reviewer Practices," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-15, September.
    2. Bornmann, Lutz & Mutz, Rüdiger & Daniel, Hans-Dieter, 2008. "Latent Markov modeling applied to grant peer review," Journal of Informetrics, Elsevier, vol. 2(3), pages 217-228.
    3. Squazzoni, Flaminio & Gandelli, Claudio, 2012. "Saint Matthew strikes again: An agent-based model of peer review and the scientific community structure," Journal of Informetrics, Elsevier, vol. 6(2), pages 265-275.
    4. C. Sean Burns & Charles W. Fox, 2017. "Language and socioeconomics predict geographic variation in peer review outcomes at an ecology journal," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 1113-1127, November.
    5. Materia, V.C. & Pascucci, S. & Kolympiris, C., 2015. "Understanding the selection processes of public research projects in agriculture: The role of scientific merit," Food Policy, Elsevier, vol. 56(C), pages 87-99.
    6. Lutz Bornmann & Ruediger Mutz & Hans-Dieter Daniel, 2007. "Row-column (RC) association model applied to grant peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 73(2), pages 139-147, November.
    7. Linton, Jonathan D., 2016. "Improving the Peer review process: Capturing more information and enabling high-risk/high-return research," Research Policy, Elsevier, vol. 45(9), pages 1936-1938.
    8. Carole J. Lee & Cassidy R. Sugimoto & Guo Zhang & Blaise Cronin, 2013. "Bias in peer review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 2-17, January.
    9. Bornmann, Lutz & Mutz, Rüdiger & Daniel, Hans-Dieter, 2008. "How to detect indications of potential sources of bias in peer review: A generalized latent variable modeling approach exemplified by a gender study," Journal of Informetrics, Elsevier, vol. 2(4), pages 280-287.

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