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The use of ChatGPT for identifying disruptive papers in science: a first exploration

Author

Listed:
  • Lutz Bornmann

    (Administrative Headquarters of the Max Planck Society)

  • Lingfei Wu

    (University of Pittsburgh)

  • Christoph Ettl

    (Administrative Headquarters of the Max Planck Society)

Abstract

ChatGPT has arrived in quantitative research evaluation. With the exploration in this Letter to the Editor, we would like to widen the spectrum of the possible use of ChatGPT in bibliometrics by applying it to identify disruptive papers. The identification of disruptive papers using publication and citation counts has become a popular topic in scientometrics. The disadvantage of the quantitative approach is its complexity in the computation. The use of ChatGPT might be an easy to use alternative.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:11:d:10.1007_s11192-024-05176-z
    DOI: 10.1007/s11192-024-05176-z
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    References listed on IDEAS

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    1. Lingfei Wu & Dashun Wang & James A. Evans, 2019. "Large teams develop and small teams disrupt science and technology," Nature, Nature, vol. 566(7744), pages 378-382, February.
    2. 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.
    3. Yiling Lin & Carl Benedikt Frey & Lingfei Wu, 2022. "Remote Collaboration Fuses Fewer Breakthrough Ideas," Papers 2206.01878, arXiv.org, revised Oct 2023.
    4. Yiling Lin & Carl Benedikt Frey & Lingfei Wu, 2023. "Remote collaboration fuses fewer breakthrough ideas," Nature, Nature, vol. 623(7989), pages 987-991, November.
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