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Assessing peer review by gauging the fate of rejected manuscripts: the case of the Journal of Artificial Societies and Social Simulation

Author

Listed:
  • Niccolò Casnici

    (University of Brescia)

  • Francisco Grimaldo

    (University of Valencia)

  • Nigel Gilbert

    (University of Surrey)

  • Pierpaolo Dondio

    (Dublin Institute of Technology)

  • Flaminio Squazzoni

    (University of Brescia)

Abstract

This paper investigates the fate of manuscripts that were rejected from JASSS-The Journal of Artificial Societies and Social Simulation, the flagship journal of social simulation. We tracked 456 manuscripts that were rejected from 1997 to 2011 and traced their subsequent publication as journal articles, conference papers or working papers. We compared the impact factor of the publishing journal and the citations of those manuscripts that were eventually published against the yearly impact factor of JASSS and the number of citations achieved by the JASSS mean and top cited articles. Only 10% of the rejected manuscripts were eventually published in a journal that was indexed in the Web of Science, although most of the rejected manuscripts were published elsewhere. Being exposed to more than one round of reviews before rejection, having received a more detailed reviewer report and being subjected to higher inter-reviewer disagreement were all associated with the number of citations received when the manuscript was eventually published. This indicates that peer review could contribute to increasing the quality even of rejected manuscripts.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:113:y:2017:i:1:d:10.1007_s11192-017-2241-1
    DOI: 10.1007/s11192-017-2241-1
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    References listed on IDEAS

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    Cited by:

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    2. Bianchi, Federico & Grimaldo, Francisco & Squazzoni, Flaminio, 2019. "The F3-index. Valuing reviewers for scholarly journals," Journal of Informetrics, Elsevier, vol. 13(1), pages 78-86.
    3. Sun, Zhuanlan, 2024. "Textual features of peer review predict top-cited papers: An interpretable machine learning perspective," Journal of Informetrics, Elsevier, vol. 18(2).
    4. Zhang, Guangyao & Xu, Shenmeng & Sun, Yao & Jiang, Chunlin & Wang, Xianwen, 2022. "Understanding the peer review endeavor in scientific publishing," Journal of Informetrics, Elsevier, vol. 16(2).
    5. Shan Jiang, 2021. "Understanding authors' psychological reactions to peer reviews: a text mining approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6085-6103, July.
    6. J. Rigby & D. Cox & K. Julian, 2018. "Journal peer review: a bar or bridge? An analysis of a paper’s revision history and turnaround time, and the effect on citation," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1087-1105, March.
    7. Balázs Győrffy & Andrea Magda Nagy & Péter Herman & Ádám Török, 2018. "Factors influencing the scientific performance of Momentum grant holders: an evaluation of the first 117 research groups," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 409-426, October.
    8. Akbaritabar, Aliakbar & Stephen, Dimity & Squazzoni, Flaminio, 2022. "A study of referencing changes in preprint-publication pairs across multiple fields," Journal of Informetrics, Elsevier, vol. 16(2).

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