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Arbitrariness in the Peer Review Process

Author

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  • Elise S. Brezis

    (Bar-Ilan University)

  • Aliaksandr Birukou

Abstract

The purpose of this paper is to analyze the causes and effects of arbitrariness in the peer review process. This paper focuses on two main reasons for the arbitrariness in peer review. The first is that referees are not homogenous and display homophily in their taste and perception of innovative ideas. The second element is that reviewers are different in the time they allocate for peer review. Our model replicates the NIPS experiment of 2014, showing that the ratings of peer review are not robust, and that altering reviewers leads to a dramatic impact on the ranking of the papers. This paper also shows that innovative works are not highly ranked in the existing peer review process, and in consequence are often rejected.

Suggested Citation

  • Elise S. Brezis & Aliaksandr Birukou, 2019. "Arbitrariness in the Peer Review Process," Working Papers 2019-08, Bar-Ilan University, Department of Economics.
    • Handle: RePEc:biu:wpaper:2019-08
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    References listed on IDEAS

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    1. Terttu Luukkonen, 2012. "Conservatism and risk-taking in peer review: Emerging ERC practices," Research Evaluation, Oxford University Press, vol. 21(1), pages 48-60, February.
    2. Elise S Brezis, 2007. "Focal randomisation: An optimal mechanism for the evaluation of R&D projects," Science and Public Policy, Oxford University Press, vol. 34(10), pages 691-698, December.
    3. Lingfei Wu & Dashun Wang & James A. Evans, 2019. "Large teams develop and small teams disrupt science and technology," Nature, Nature, vol. 566(7744), pages 378-382, February.
    4. Flaminio Squazzoni & Elise Brezis & Ana Marušić, 2017. "Scientometrics of peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 501-502, October.
    5. 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.
    6. Kevin J. Boudreau & Eva C. Guinan & Karim R. Lakhani & Christoph Riedl, 2016. "Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance, Novelty, and Resource Allocation in Science," Management Science, INFORMS, vol. 62(10), pages 2765-2783, October.
    7. Kevin Gross & Carl T Bergstrom, 2019. "Contest models highlight inherent inefficiencies of scientific funding competitions," PLOS Biology, Public Library of Science, vol. 17(1), pages 1-15, January.
    8. Azzurra Ragone & Katsiaryna Mirylenka & Fabio Casati & Maurizio Marchese, 2013. "On peer review in computer science: analysis of its effectiveness and suggestions for improvement," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 317-356, November.
    9. Christoph Bartneck, 2017. "Reviewers’ scores do not predict impact: bibliometric analysis of the proceedings of the human–robot interaction conference," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 179-194, January.
    10. repec:nas:journl:v:115:y:2018:p:2952-2957 is not listed on IDEAS
    11. Michail Kovanis & Ludovic Trinquart & Philippe Ravaud & Raphaël Porcher, 2017. "Evaluating alternative systems of peer review: a large-scale agent-based modelling approach to scientific publication," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 651-671, October.
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    Cited by:

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    2. Sven Helmer & David B. Blumenthal & Kathrin Paschen, 2020. "What is meaningful research and how should we measure it?," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 153-169, October.
    3. Carol Nash, 2023. "Roles and Responsibilities for Peer Reviewers of International Journals," Publications, MDPI, vol. 11(2), pages 1-24, June.
    4. Pengfei Jia & Weixi Xie & Guangyao Zhang & Xianwen Wang, 2023. "Do reviewers get their deserved acknowledgments from the authors of manuscripts?," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5687-5703, October.
    5. Elena A. Erosheva & Patrícia Martinková & Carole J. Lee, 2021. "When zero may not be zero: A cautionary note on the use of inter‐rater reliability in evaluating grant peer review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 904-919, July.
    6. Tirthankar Ghosal & Sandeep Kumar & Prabhat Kumar Bharti & Asif Ekbal, 2022. "Peer review analyze: A novel benchmark resource for computational analysis of peer reviews," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-29, January.
    7. Eva Barlösius & Laura Paruschke & Axel Philipps, 2024. "Peer review’s irremediable flaws: Scientists’ perspectives on grant evaluation in Germany," Research Evaluation, Oxford University Press, vol. 32(4), pages 623-634.
    8. Jibang Wu & Haifeng Xu & Yifan Guo & Weijie Su, 2023. "A Truth Serum for Eliciting Self-Evaluations in Scientific Reviews," Papers 2306.11154, arXiv.org, revised Feb 2024.
    9. Axel Philipps, 2022. "Research funding randomly allocated? A survey of scientists’ views on peer review and lottery," Science and Public Policy, Oxford University Press, vol. 49(3), pages 365-377.

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    More about this item

    Keywords

    arbitrariness; homophily; peer review; innovation;
    All these keywords.

    JEL classification:

    • D73 - Microeconomics - - Analysis of Collective Decision-Making - - - Bureaucracy; Administrative Processes in Public Organizations; Corruption
    • G01 - Financial Economics - - General - - - Financial Crises
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation

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