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Specification uncertainty: what the disruption index tells us about the (hidden) multiverse of bibliometric indicators

Author

Listed:
  • Christian Leibel

    (Administrative Headquarters of the Max Planck Society
    Ludwig-Maximilians-Universität München)

  • Lutz Bornmann

    (Administrative Headquarters of the Max Planck Society)

Abstract

Following Funk and Owen-Smith (Manag Sci 63:791–817, 2017), Wu et al. (Nature 566:378–382, 2019) proposed the disruption index (DI1) as a bibliometric indicator that measures disruptive and consolidating research. When we summarized the literature on the disruption index for our recently published review article (Leibel and Bornmann in Scientometrics 129:601–639, 2024), we noticed that the calculation of disruption scores comes with numerous (hidden) degrees of freedom. In this Letter to the Editor, we explain based on the DI1 (as an example) why the analytical flexibility of bibliometric indicators potentially endangers the credibility of research and advertise the application of multiverse-style methods to increase the transparency of the research.

Suggested Citation

  • Christian Leibel & Lutz Bornmann, 2024. "Specification uncertainty: what the disruption index tells us about the (hidden) multiverse of bibliometric indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(12), pages 7971-7979, December.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:12:d:10.1007_s11192-024-05201-1
    DOI: 10.1007/s11192-024-05201-1
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    References listed on IDEAS

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    1. Michael Park & Erin Leahey & Russell J. Funk, 2023. "Papers and patents are becoming less disruptive over time," Nature, Nature, vol. 613(7942), pages 138-144, January.
    2. Yiling Lin & Carl Benedikt Frey & Lingfei Wu, 2022. "Remote Collaboration Fuses Fewer Breakthrough Ideas," Papers 2206.01878, arXiv.org, revised Oct 2023.
    3. Yiling Lin & Carl Benedikt Frey & Lingfei Wu, 2023. "Remote collaboration fuses fewer breakthrough ideas," Nature, Nature, vol. 623(7989), pages 987-991, November.
    4. Russell J. Funk & Jason Owen-Smith, 2017. "A Dynamic Network Measure of Technological Change," Management Science, INFORMS, vol. 63(3), pages 791-817, March.
    5. Guoqiang Liang & Ying Lou & Haiyan Hou, 2022. "Revisiting the disruptive index: evidence from the Nobel Prize-winning articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5721-5730, October.
    6. Nan Deng & An Zeng, 2023. "Enhancing the robustness of the disruption metric against noise," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2419-2428, April.
    7. Nick Huntington‐Klein & Andreu Arenas & Emily Beam & Marco Bertoni & Jeffrey R. Bloem & Pralhad Burli & Naibin Chen & Paul Grieco & Godwin Ekpe & Todd Pugatch & Martin Saavedra & Yaniv Stopnitzky, 2021. "The influence of hidden researcher decisions in applied microeconomics," Economic Inquiry, Western Economic Association International, vol. 59(3), pages 944-960, July.
    8. Jiexun Li & Jiyao Chen, 2022. "Measuring destabilization and consolidation in scientific knowledge evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5819-5839, October.
    9. Lutz Bornmann & Alexander Tekles, 2019. "Disruptive papers published in Scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 331-336, July.
    10. Daniel Bischof, 2017. "New graphic schemes for Stata: plotplain and plottig," Stata Journal, StataCorp LLC, vol. 17(3), pages 748-759, September.
    11. 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.
    12. Libo Sheng & Dongqing Lyu & Xuanmin Ruan & Hongquan Shen & Ying Cheng, 2023. "The association between prior knowledge and the disruption of an article," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4731-4751, August.
    13. Shiyun Wang & Yaxue Ma & Jin Mao & Yun Bai & Zhentao Liang & Gang Li, 2023. "Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(2), pages 150-167, February.
    14. Chris Chatfield, 1995. "Model Uncertainty, Data Mining and Statistical Inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(3), pages 419-444, May.
    15. Bornmann, Lutz & Tekles, Alexander, 2021. "Convergent validity of several indicators measuring disruptiveness with milestone assignments to physics papers by experts," Journal of Informetrics, Elsevier, vol. 15(3).
    16. Christian Leibel & Lutz Bornmann, 2024. "What do we know about the disruption index in scientometrics? An overview of the literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(1), pages 601-639, January.
    17. Leamer, Edward E, 1985. "Sensitivity Analyses Would Help," American Economic Review, American Economic Association, vol. 75(3), pages 308-313, June.
    18. Leydesdorff, Loet & Bornmann, Lutz, 2021. "Disruption indices and their calculation using web-of-science data: Indicators of historical developments or evolutionary dynamics?," Journal of Informetrics, Elsevier, vol. 15(4).
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