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Editorial decisions with informed and uninformed reviewers

Author

Listed:
  • Rosa Rodriguez-Sánchez

    (Universidad de Granada)

  • J. A. García

    (Universidad de Granada)

  • J. Fdez-Valdivia

    (Universidad de Granada)

Abstract

In this paper, the interaction of the reviewers is analyzed in the context of the open peer review. There, the reviewer group can exchange comments on the manuscript. This group of experts is called the informed reviewer group because, with the exchange of comments, each reviewer knows about the trends in the evaluation of the manuscript. Then, we show that, in a Bayesian Nash equilibrium, the probability that the editor chooses to accept or reject is the same. In this paper, this reviewer group is also compared to a group of uninformed reviewers in the classic review system. In this second case, the uninformed reviewers only communicate with the journal editor. They always believe that their recommendation is in the majority, and the probability that the uninformed reviewers abstain from making any recommendation increases as the number of reviewers grows. In some open access journals, the editor can obtain recommendations by both uninformed and informed reviewers groups. In this situation, the editor’s decision could depend on the order followed by the editor in the analysis of the recommendations of each reviewers group (informed and uninformed). A free online tool has been created to help the editor with this process.

Suggested Citation

  • Rosa Rodriguez-Sánchez & J. A. García & J. Fdez-Valdivia, 2018. "Editorial decisions with informed and uninformed reviewers," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 25-43, October.
    • Handle: RePEc:spr:scient:v:117:y:2018:i:1:d:10.1007_s11192-018-2875-7
    DOI: 10.1007/s11192-018-2875-7
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    References listed on IDEAS

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    1. J. A. García & Rosa Rodriguez-Sánchez & J. Fdez-Valdivia, 2016. "Why the referees’ reports I receive as an editor are so much better than the reports I receive as an author?," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 967-986, March.
    2. 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.
    3. Lutz Bornmann, 2012. "The Hawthorne effect in journal peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 857-862, June.
    4. Carole J. Lee & Cassidy R. Sugimoto & Guo Zhang & Blaise Cronin, 2013. "Bias in peer review," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(1), pages 2-17, January.
    5. Taylor, Curtis R. & Yildirim, Huseyin, 2010. "Public information and electoral bias," Games and Economic Behavior, Elsevier, vol. 68(1), pages 353-375, January.
    6. Lutz Bornmann, 2014. "Gould , T.H.P. ( 2013 ). Do we still need peer review? An argument for change . Scarecrow Press : Plymouth, UK," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(1), pages 209-213, January.
    7. Jose A. García & Rosa Rodriguez-Sánchez & Joaquín Fdez-Valdivia, 2015. "Adverse selection of reviewers," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(6), pages 1252-1262, June.
    8. J. A. García & Rosa Rodriguez-Sánchez & J. Fdez-Valdivia, 2017. "Problems with open participation in peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1881-1885, September.
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