A systematic approach to assessing the accuracy of a peer review process in an academic journal

J. A. García (), Rosa Rodriguez-Sánchez () and J. Fdez-Valdivia ()
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J. A. García: CITIC-UGR, Universidad de Granada, Departamento de Ciencias de la Computación e I. A.
Rosa Rodriguez-Sánchez: CITIC-UGR, Universidad de Granada, Departamento de Ciencias de la Computación e I. A.
J. Fdez-Valdivia: CITIC-UGR, Universidad de Granada, Departamento de Ciencias de la Computación e I. A.

Scientometrics, 2025, vol. 130, issue 11, No 17, 6323-6347

Abstract: Abstract In this paper, based on Bayes’ theorem, we describe a systematic approach to assessing the accuracy of a peer review process in a given journal. After receiving the reviewers’ recommendations, an editor evaluates their reports to obtain an acceptance or rejection signal to decide the manuscript’s future. In our model, an ‘acceptance signal’ refers to an affirmative indication from the reviewers that a manuscript is suitable for publication in a given journal. A ‘rejection signal’ refers to a negative indication. In this scenario, the predictive value of an acceptance signal (PVA) tells us how many acceptance signals correspond to quality manuscripts that meet the journal’s publication standards. Meanwhile, the predictive value of a rejection signal (PVR) tells us how many rejection signals correspond to manuscripts that do not meet the journal’s publication standards. This calculation is essential because a direct interpretation of an editorial decision can be misleading. Our approach has modeled different types of computer science and open-access journals. The highest-impact computer science journals achieved acceptable PVA and PVR values for high, medium, and low peer-review costs. Academic journals with better reviewers and editors will have lower peer-review costs. Open-access journals that strive to be world-class and highly reputable mega-journals also achieved acceptable PVA and PVR values for high, medium, and low peer-review costs. On the contrary, we found that lower-impact journals with high peer-review costs achieved unacceptable predictive values. To improve the predictive value of acceptance and rejection signals, especially in journals with low acceptance rates, it is recommended that all submitted manuscripts undergo a rigorous desk decision process, significantly increasing the likelihood of acceptance of manuscripts sent out for external review. Academic journals with high peer review costs are also advised to select better editors and reviewers. This will be especially relevant in review processes involving particularly complex manuscripts. Finally, journals should strive to achieve the greatest possible impact, especially regarding profits and losses from editorial decisions.

Keywords: Peer Review; Predictive Value; Bayesian Approach; Acceptance Rate; Sensitivity; Specificity (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11192-025-05452-6

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