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An ablation study on the use of publication venue quality to rank computer science departments

Author

Listed:
  • Aniruddha Maiti

    (Temple University)

  • Sai Shi

    (Temple University)

  • Slobodan Vucetic

    (Temple University)

Abstract

This paper focuses on ranking computer science departments based on the quality of publications by the faculty in those departments. There are multiple strategies to convert publication lists into ranking scores for the departments. Important open questions include handling multi-author publications, inclusion criteria for publications and publication venues, accounting for the quality of publication venues, and accounting for the sub-areas of computer science. An ablation study is performed to evaluate the importance of different decisions for department ranking. The correlation between the resulting rankings and the peer assessment of computer science departments provided by the U.S. News was measured to evaluate the importance of different decisions. The results show that the selection of publication venues has the highest impact on the ranking. In contrast, decisions related to publication recency, multi-author publications, and clustering publications into subareas have less impact. Overall, Pearson’s correlation coefficient between the publication-based scores and the U.S. News ranking is above 0.90 for a large range of decisions, indicating a strong agreement between the objective measure and the subjective opinion of peers.

Suggested Citation

  • Aniruddha Maiti & Sai Shi & Slobodan Vucetic, 2023. "An ablation study on the use of publication venue quality to rank computer science departments," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4197-4218, August.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:8:d:10.1007_s11192-023-04733-2
    DOI: 10.1007/s11192-023-04733-2
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    References listed on IDEAS

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    1. Leo Egghe, 2006. "Theory and practise of the g-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 131-152, October.
    2. Judit Bar-Ilan, 2010. "Web of Science with the Conference Proceedings Citation Indexes: the case of computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 809-824, June.
    3. Jaime A. Teixeira da Silva & Aamir Raoof Memon, 2017. "CiteScore: A cite for sore eyes, or a valuable, transparent metric?," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 553-556, April.
    4. Masaru Kuno & Mary Prorok & Shubin Zhang & Huy Huynh & Thurston Miller, 2022. "Deciphering the US News and World Report Ranking of US Chemistry Graduate Programs," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2131-2150, May.
    5. Declan Butler, 2011. "Computing giants launch free science metrics," Nature, Nature, vol. 476(7358), pages 18-18, August.
    6. Michael J. Kurtz & Edwin A. Henneken, 2017. "Measuring metrics - a 40-year longitudinal cross-validation of citations, downloads, and peer review in astrophysics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(3), pages 695-708, March.
    7. Themis Lazaridis, 2010. "Ranking university departments using the mean h-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 211-216, February.
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