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Studying grant decision-making: a linguistic analysis of review reports

Author

Listed:
  • Peter van den Besselaar

    (Vrije Universiteit Amsterdam
    TMC)

  • Ulf Sandström

    (KTH Royal Institute of Technology)

  • Hélène Schiffbaenker

    (Joanneum Research)

Abstract

Peer and panel review are the dominant forms of grant decision-making, despite its serious weaknesses as shown by many studies. This paper contributes to the understanding of the grant selection process through a linguistic analysis of the review reports. We reconstruct in that way several aspects of the evaluation and selection process: what dimensions of the proposal are discussed during the process and how, and what distinguishes between the successful and non-successful applications? We combine the linguistic findings with interviews with panel members and with bibliometric performance scores of applicants. The former gives the context, and the latter helps to interpret the linguistic findings. The analysis shows that the performance of the applicant and the content of the proposed study are assessed with the same categories, suggesting that the panelists actually do not make a difference between past performance and promising new research ideas. The analysis also suggests that the panels focus on rejecting the applications by searching for weak points, and not on finding the high-risk/high-gain groundbreaking ideas that may be in the proposal. This may easily result in sub-optimal selections, in low predictive validity, and in bias.

Suggested Citation

  • Peter van den Besselaar & Ulf Sandström & Hélène Schiffbaenker, 2018. "Studying grant decision-making: a linguistic analysis of review reports," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 313-329, October.
  • Handle: RePEc:spr:scient:v:117:y:2018:i:1:d:10.1007_s11192-018-2848-x
    DOI: 10.1007/s11192-018-2848-x
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    References listed on IDEAS

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    1. Meike Olbrecht & Lutz Bornmann, 2010. "Panel peer review of grant applications: what do we know from research in social psychology on judgment and decision-making in groups?," Research Evaluation, Oxford University Press, vol. 19(4), pages 293-304, October.
    2. van den Besselaar, Peter & Sandström, Ulf, 2015. "Early career grants, performance, and careers: A study on predictive validity of grant decisions," Journal of Informetrics, Elsevier, vol. 9(4), pages 826-838.
    3. Pleun van Arensbergen & Inge van der Weijden & Peter van den Besselaar, 2014. "The selection of talent as a group process. A literature review on the social dynamics of decision making in grant panels," Research Evaluation, Oxford University Press, vol. 23(4), pages 298-311.
    4. Qi Wang & Ulf Sandström, 2015. "Defining the role of cognitive distance in the peer review process with an explorative study of a grant scheme in infection biology," Research Evaluation, Oxford University Press, vol. 24(3), pages 271-281.
    5. Bornmann, Lutz & Leydesdorff, Loet & Van den Besselaar, Peter, 2010. "A meta-evaluation of scientific research proposals: Different ways of comparing rejected to awarded applications," Journal of Informetrics, Elsevier, vol. 4(3), pages 211-220.
    6. Peter van den Besselaar & Ulf Sandström, 2016. "Gender differences in research performance and its impact on careers: a longitudinal case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 143-162, January.
    7. Peter van den Besselaar & Loet Leydesdorff, 2009. "Past performance, peer review and project selection: a case study in the social and behavioral sciences," Research Evaluation, Oxford University Press, vol. 18(4), pages 273-288, October.
    8. Liv Langfeldt, 2004. "Expert panels evaluating research: decision-making and sources of bias," Research Evaluation, Oxford University Press, vol. 13(1), pages 51-62, April.
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    Cited by:

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    8. Emre Özel, 2024. "What is Gender Bias in Grant Peer review?," Working Papers halshs-03862027, HAL.
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    11. Horbach, Serge P.J.M. & Schneider, Jesper W. & Sainte-Marie, Maxime, 2022. "Ungendered writing: Writing styles are unlikely to account for gender differences in funding rates in the natural and technical sciences," Journal of Informetrics, Elsevier, vol. 16(4).
    12. Seeber, Marco & Alon, Ilan & Pina, David G. & Piro, Fredrik Niclas & Seeber, Michele, 2022. "Predictors of applying for and winning an ERC Proof-of-Concept grant: An automated machine learning model," Technological Forecasting and Social Change, Elsevier, vol. 184(C).

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