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Are the confidence scores of reviewers consistent with the review content? Evidence from top conference proceedings in AI

Author

Listed:
  • Wenqing Wu

    (Nanjing University of Science and Technology)

  • Haixu Xi

    (Nanjing University of Science and Technology)

  • Chengzhi Zhang

    (Nanjing University of Science and Technology)

Abstract

Peer review is a critical process used in academia to assess the quality and validity of research articles. Top-tier conferences in the field of artificial intelligence (e.g. ICLR and ACL et al.) require reviewers to provide confidence scores to ensure the reliability of their review reports. However, existing studies on confidence scores have neglected to measure the consistency between the comment text and the confidence score in a more refined way, which may overlook more detailed details (such as aspects) in the text, leading to incomplete understanding of the results and insufficient objective analysis of the results. In this work, we propose assessing the consistency between the textual content of the review reports and the assigned scores at a fine-grained level, including word, sentence and aspect levels. The data used in this paper is derived from the peer review comments of conferences in the fields of deep learning and natural language processing. We employed deep learning models to detect hedge sentences and their corresponding aspects. Furthermore, we conducted statistical analyses of the length of review reports, frequency of hedge word usage, number of hedge sentences, frequency of aspect mentions, and their associated sentiment to assess the consistency between the textual content and confidence scores. Finally, we performed correlation analysis, significance tests and regression analysis on the data to examine the impact of confidence scores on the outcomes of the papers. The results indicate that textual content of the review reports and their confidence scores have high level of consistency at the word, sentence, and aspect levels. The regression results reveal a negative correlation between confidence scores and paper outcomes, indicating that higher confidence scores given by reviewers were associated with paper rejection. This indicates that current overall assessment of the paper’s content and quality by the experts is reliable, making the transparency and fairness of the peer review process convincing. We release our data and associated codes at https://github.com/njust-winchy/confidence_score .

Suggested Citation

  • Wenqing Wu & Haixu Xi & Chengzhi Zhang, 2024. "Are the confidence scores of reviewers consistent with the review content? Evidence from top conference proceedings in AI," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4109-4135, July.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:7:d:10.1007_s11192-024-05070-8
    DOI: 10.1007/s11192-024-05070-8
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    References listed on IDEAS

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    1. Sun, Zhuanlan & Clark Cao, C. & Ma, Chao & Li, Yiwei, 2023. "The academic status of reviewers predicts their language use," Journal of Informetrics, Elsevier, vol. 17(4).
    2. Peter Kardos & Ádám Kun & Csaba Pléh & Ferenc Jordán, 2023. "(How) should researchers publicize their research papers before peer review?," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 2019-2023, March.
    3. Flaminio Squazzoni & Elise Brezis & Ana Marušić, 2017. "Scientometrics of peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 501-502, October.
    4. Ying He & Kun Tian & Xiaoran Xu, 2023. "A validation study on the factors affecting the practice modes of open peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 587-607, January.
    5. Adrian Mulligan & Louise Hall & Ellen Raphael, 2013. "Peer review in a changing world: An international study measuring the attitudes of researchers," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 132-161, January.
    6. Guangyao Zhang & Yuqi Wang & Weixi Xie & Han Du & Chunlin Jiang & Xianwen Wang, 2021. "The open access usage advantage: a temporal and spatial analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6187-6199, July.
    7. 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.
    8. Lutz Bornmann & Rüdiger Mutz, 2015. "Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2215-2222, November.
    9. Janine Huisman & Jeroen Smits, 2017. "Duration and quality of the peer review process: the author’s perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 633-650, October.
    10. Vladimir Batagelj & Anuška Ferligoj & Flaminio Squazzoni, 2017. "The emergence of a field: a network analysis of research on peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 503-532, October.
    11. Adrian Mulligan & Louise Hall & Ellen Raphael, 2013. "Peer review in a changing world: An international study measuring the attitudes of researchers," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(1), pages 132-161, January.
    12. Marco Seeber & Alberto Bacchelli, 2017. "Does single blind peer review hinder newcomers?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 567-585, October.
    13. Yuxian Liu & Ronald Rousseau, 2023. "A proposal for the peer review procedure for funding decisions," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 861-865, January.
    14. 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).
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