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On peer review in computer science: analysis of its effectiveness and suggestions for improvement

Author

Listed:
  • Azzurra Ragone

    (University of Trento)

  • Katsiaryna Mirylenka

    (University of Trento)

  • Fabio Casati

    (University of Trento)

  • Maurizio Marchese

    (University of Trento)

Abstract

In this paper we focus on the analysis of peer reviews and reviewers behaviour in a number of different review processes. More specifically, we report on the development, definition and rationale of a theoretical model for peer review processes to support the identification of appropriate metrics to assess the processes main characteristics in order to render peer review more transparent and understandable. Together with known metrics and techniques we introduce new ones to assess the overall quality (i.e. ,reliability, fairness, validity) and efficiency of peer review processes e.g. the robustness of the process, the degree of agreement/disagreement among reviewers, or positive/negative bias in the reviewers’ decision making process. We also check the ability of peer review to assess the impact of papers in subsequent years. We apply the proposed model and analysis framework to a large reviews data set from ten different conferences in computer science for a total of ca. 9,000 reviews on ca. 2,800 submitted contributions. We discuss the implications of the results and their potential use toward improving the analysed peer review processes. A number of interesting results were found, in particular: (1) a low correlation between peer review outcome and impact in time of the accepted contributions; (2) the influence of the assessment scale on the way how reviewers gave marks; (3) the effect and impact of rating bias, i.e. reviewers who constantly give lower/higher marks w.r.t. all other reviewers; (4) the effectiveness of statistical approaches to optimize some process parameters (e.g. ,number of papers per reviewer) to improve the process overall quality while maintaining the overall effort under control. Based on the lessons learned, we suggest ways to improve the overall quality of peer-review through procedures that can be easily implemented in current editorial management systems.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:97:y:2013:i:2:d:10.1007_s11192-013-1002-z
    DOI: 10.1007/s11192-013-1002-z
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    References listed on IDEAS

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    2. Mund, Carolin & Neuhäusler, Peter, 2015. "Towards an early-stage identification of emerging topics in science—The usability of bibliometric characteristics," Journal of Informetrics, Elsevier, vol. 9(4), pages 1018-1033.
    3. Mario Paolucci & Francisco Grimaldo, 2014. "Mechanism change in a simulation of peer review: from junk support to elitism," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(3), pages 663-688, June.
    4. Niccolò Casnici & Francisco Grimaldo & Nigel Gilbert & Pierpaolo Dondio & Flaminio Squazzoni, 2017. "Assessing peer review by gauging the fate of rejected manuscripts: the case of the Journal of Artificial Societies and Social Simulation," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 533-546, October.
    5. Flaminio Squazzoni & Elise Brezis & Ana Marušić, 2017. "Scientometrics of peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 501-502, October.
    6. Marco Seeber & Alberto Bacchelli, 2017. "Does single blind peer review hinder newcomers?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 567-585, October.
    7. Monica Aniela Zaharie & Marco Seeber, 2018. "Are non-monetary rewards effective in attracting peer reviewers? A natural experiment," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1587-1609, December.

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