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Expert panels evaluating research: decision-making and sources of bias

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  • Liv Langfeldt

Abstract

This study consists of in-depth analysis of six international evaluations of Norwegian research. There was little overlapping competence on the panels, a high degree of task division and the composition of an expert panel, the organisation of its work and lack of group interaction, may have been decisive for the conclusions of the evaluations. Moreover, there seems to have been a serious disparity between the processes and resources of the studied evaluations and the demands that ideally should be met when judging scholarly quality. The revealed weaknesses are believed to be inherent to the concept of expert panel evaluation of research as an instrument for national research policy, and not specific for the studied evaluations. Copyright , Beech Tree Publishing.

Suggested Citation

  • 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.
    • Handle: RePEc:oup:rseval:v:13:y:2004:i:1:p:51-62
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    Cited by:

    1. Andrew Dustan & Stanislao Maldonado & Juan Manuel Hernandez-Agramonte, 2018. "Motivating bureaucrats with non-monetary incentives when state capacity is weak: Evidence from large-scale field experiments in Peru," Working Papers 136, Peruvian Economic Association.
    2. Andrew Dustan & Juan Manuel Hernandez-Agramonte & Stanislao Maldonado, 2018. "Motivating bureaucrats with non-monetary incentives when state capacity is weak: Evidence from large-scale," Natural Field Experiments 00664, The Field Experiments Website.
    3. A. Gaunand & L. Colinet & P.-B. Joly & M. Matt, 2022. "Counting what really counts? Assessing the political impact of science," The Journal of Technology Transfer, Springer, vol. 47(3), pages 699-721, June.
    4. Benda, Wim G.G. & Engels, Tim C.E., 2011. "The predictive validity of peer review: A selective review of the judgmental forecasting qualities of peers, and implications for innovation in science," International Journal of Forecasting, Elsevier, vol. 27(1), pages 166-182.
    5. Thomas Feliciani & Junwen Luo & Lai Ma & Pablo Lucas & Flaminio Squazzoni & Ana Marušić & Kalpana Shankar, 2019. "A scoping review of simulation models of peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 555-594, October.
    6. Manuel Carpio & Andrés J. Prieto, 2021. "Expert Panel, Preventive Maintenance of Heritage Buildings and Fuzzy Logic System: An Application in Valdivia, Chile," Sustainability, MDPI, vol. 13(12), pages 1-15, June.
    7. Doyeon Lee & Keunhwan Kim, 2022. "Public R&D Projects-Based Investment and Collaboration Framework for an Overarching South Korean National Strategy of Personalized Medicine," IJERPH, MDPI, vol. 19(3), pages 1-25, January.
    8. Sheheryar Banuri & Stefan Dercon & Varun Gauri, 2019. "Biased Policy Professionals," The World Bank Economic Review, World Bank, vol. 33(2), pages 310-327.
    9. 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.
    10. van den Besselaar, Peter, 2012. "Selection committee membership: Service or self-service," Journal of Informetrics, Elsevier, vol. 6(4), pages 580-585.
    11. Rahman, A.I.M. Jakaria & Guns, Raf & Rousseau, Ronald & Engels, Tim C.E., 2015. "Is the expertise of evaluation panels congruent with the research interests of the research groups: A quantitative approach based on barycenters," Journal of Informetrics, Elsevier, vol. 9(4), pages 704-721.
    12. Benda, Wim G.G. & Engels, Tim C.E., 2011. "The predictive validity of peer review: A selective review of the judgmental forecasting qualities of peers, and implications for innovation in science," International Journal of Forecasting, Elsevier, vol. 27(1), pages 166-182, January.
    13. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    14. Andrea Bonaccorsi & Nicola Melluso & Francesco Alessandro Massucci, 2022. "Exploring the antecedents of interdisciplinarity at the European Research Council: a topic modeling approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 6961-6991, December.

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