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Why it has become more difficult to predict Nobel Prize winners: a bibliometric analysis of nominees and winners of the chemistry and physics prizes (1901–2007)

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  • Yves Gingras

    (Université du Québec à Montréal)

  • Matthew L. Wallace

    (Université du Québec à Montréal)

Abstract

We propose a comprehensive bibliometric study of the profile of Nobel Prize winners in chemistry and physics from 1901 to 2007, based on citation data available over the same period. The data allows us to observe the evolution of the profiles of winners in the years leading up to—and following—nominations and awarding of the Nobel Prize. The degree centrality and citation rankings in these fields confirm that the Prize is awarded at the peak of the winners’ citation history, despite a brief Halo Effect observable in the years following the attribution of the Prize. Changes in the size and organization of the two fields result in a rapid decline of predictive power of bibliometric data over the century. This can be explained not only by the growing size and fragmentation of the two disciplines, but also, at least in the case of physics, by an implicit hierarchy in the most legitimate topics within the discipline, as well as among the scientists selected for the Nobel Prize. Furthermore, the lack of readily-identifiable dominant contemporary physicists suggests that there are few new paradigm shifts within the field, as perceived by the scientific community as a whole.

Suggested Citation

  • Yves Gingras & Matthew L. Wallace, 2010. "Why it has become more difficult to predict Nobel Prize winners: a bibliometric analysis of nominees and winners of the chemistry and physics prizes (1901–2007)," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 401-412, February.
  • Handle: RePEc:spr:scient:v:82:y:2010:i:2:d:10.1007_s11192-009-0035-9
    DOI: 10.1007/s11192-009-0035-9
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    1. Romualdas Karazija & Alina Momkauskaitė, 2004. "The Nobel prize in physics - regularities and tendencies," Scientometrics, Springer;Akadémiai Kiadó, vol. 61(2), pages 191-205, October.
    2. B. S. Kademani & V. L. Kalyane & Vijai Kumar & Lalit Mohan, 2005. "Nobel laureates: Their publication productivity, collaboration and authorship status," Scientometrics, Springer;Akadémiai Kiadó, vol. 62(2), pages 261-268, January.
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    Cited by:

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    10. Dag W. Aksnes & Liv Langfeldt & Paul Wouters, 2019. "Citations, Citation Indicators, and Research Quality: An Overview of Basic Concepts and Theories," SAGE Open, , vol. 9(1), pages 21582440198, February.
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    15. Zehra Taşkın, 2021. "Forecasting the future of library and information science and its sub-fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1527-1551, February.
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    18. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
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    20. Elisabeth Maria Schlagberger & Lutz Bornmann & Johann Bauer, 2016. "At what institutions did Nobel laureates do their prize-winning work? An analysis of biographical information on Nobel laureates from 1994 to 2014," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 723-767, November.
    21. Gerhard A. Wuehrer & Angela Elisabeth Smejkal, 2013. "The knowledge domain of the academy of international business studies (AIB) conferences: a longitudinal scientometric perspective for the years 2006–2011," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(2), pages 541-561, May.
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    24. Betancourt, Nathan & Jochem, Torsten & Otner, Sarah M.G., 2023. "Standing on the shoulders of giants: How star scientists influence their coauthors," Research Policy, Elsevier, vol. 52(1).
    25. Bilal Barış Alkan & Leyla Karakuş & Bekir Direkci, 2023. "Knowledge discovery from the texts of Nobel Prize winners in literature: sentiment analysis and Latent Dirichlet Allocation," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5311-5334, September.

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