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The use of ChatGPT to find similar institutions for institutional benchmarking

Author

Listed:
  • Lutz Bornmann

    (Administrative Headquarters of the Max Planck Society)

  • Benedetto Lepori

    (Università Della Svizzera Italiana)

Abstract

In evaluative bibliometrics and higher education studies, one is frequently confronted with the task of comparing institutions with similar institutions. In this Letter to the Editor, a simple approach is discussed which applies ChatGPT. Although the approach seems to produce promising results (tested with an example at the level of research institute and of a university), it is necessary to investigate it systematically based on a sample including many institutions before it should be applied in research evaluation. The challenge in systematic investigations is that ChatGPT provides the user with different answers on the sane request (missing reliability).

Suggested Citation

  • Lutz Bornmann & Benedetto Lepori, 2024. "The use of ChatGPT to find similar institutions for institutional benchmarking," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3593-3598, June.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:6:d:10.1007_s11192-024-05039-7
    DOI: 10.1007/s11192-024-05039-7
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    References listed on IDEAS

    as
    1. Lutz Bornmann & Felix Moya Anegón & Rüdiger Mutz, 2013. "Do universities or research institutions with a specific subject profile have an advantage or a disadvantage in institutional rankings?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(11), pages 2310-2316, November.
    2. Daraio, Cinzia & Bonaccorsi, Andrea & Simar, Léopold, 2015. "Rankings and university performance: A conditional multidimensional approach," European Journal of Operational Research, Elsevier, vol. 244(3), pages 918-930.
    3. Qi Wang & Tobias Jeppsson, 2022. "Identifying benchmark units for research management and evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7557-7574, December.
    4. Nicolas Carayol & Ghislaine Filliatreau & Agenor Lahatte, 2012. "Reference classes: a tool for benchmarking universities’ research," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 351-371, November.
    5. E.C.M. Noyons & H.F. Moed & M. Luwel, 1999. "Combining mapping and citation analysis for evaluative bibliometric purposes: A bibliometric study," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(2), pages 115-131.
    6. Lutz Bornmann & Felix de Moya Anegón & Rüdiger Mutz, 2013. "Do Universities or Research Institutions With a Specific Subject Profile Have an Advantage or a Disadvantage in Institutional Rankings? A Latent Class Analysis With Data From the SCImago Ranking," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(11), pages 2310-2316, November.
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    Cited by:

    1. Giulio Giacomo Cantone, 2024. "How to measure interdisciplinary research? A systemic design for the model of measurement," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(8), pages 4937-4982, August.
    2. Lutz Bornmann & Lingfei Wu & Christoph Ettl, 2024. "The use of ChatGPT for identifying disruptive papers in science: a first exploration," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7161-7165, November.

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