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The information value of early career productivity in mathematics: a ROC analysis of prediction errors in bibliometricly informed decision making

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  • Jonas Lindahl

    (Umeå University)

  • Rickard Danell

    (Umeå University)

Abstract

The aim of this study was to provide a framework to evaluate bibliometric indicators as decision support tools from a decision making perspective and to examine the information value of early career publication rate as a predictor of future productivity. We used ROC analysis to evaluate a bibliometric indicator as a tool for binary decision making. The dataset consisted of 451 early career researchers in the mathematical sub-field of number theory. We investigated the effect of three different definitions of top performance groups—top 10, top 25, and top 50 %; the consequences of using different thresholds in the prediction models; and the added prediction value of information on early career research collaboration and publications in prestige journals. We conclude that early career performance productivity has an information value in all tested decision scenarios, but future performance is more predictable if the definition of a high performance group is more exclusive. Estimated optimal decision thresholds using the Youden index indicated that the top 10 % decision scenario should use 7 articles, the top 25 % scenario should use 7 articles, and the top 50 % should use 5 articles to minimize prediction errors. A comparative analysis between the decision thresholds provided by the Youden index which take consequences into consideration and a method commonly used in evaluative bibliometrics which do not take consequences into consideration when determining decision thresholds, indicated that differences are trivial for the top 25 and the 50 % groups. However, a statistically significant difference between the methods was found for the top 10 % group. Information on early career collaboration and publication strategies did not add any prediction value to the bibliometric indicator publication rate in any of the models. The key contributions of this research is the focus on consequences in terms of prediction errors and the notion of transforming uncertainty into risk when we are choosing decision thresholds in bibliometricly informed decision making. The significance of our results are discussed from the point of view of a science policy and management.

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  • Jonas Lindahl & Rickard Danell, 2016. "The information value of early career productivity in mathematics: a ROC analysis of prediction errors in bibliometricly informed decision making," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 2241-2262, December.
    • Handle: RePEc:spr:scient:v:109:y:2016:i:3:d:10.1007_s11192-016-2097-9
    DOI: 10.1007/s11192-016-2097-9
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    References listed on IDEAS

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    1. Pierre Dubois & Jean-Charles Rochet & Jean-Marc Schlenker, 2014. "Productivity and mobility in academic research: evidence from mathematicians," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1669-1701, March.
    2. Rickard Danell, 2011. "Can the quality of scientific work be predicted using information on the author's track record?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(1), pages 50-60, January.
    3. Frank Havemann & Birger Larsen, 2015. "Bibliometric indicators of young authors in astrophysics: Can later stars be predicted?," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1413-1434, February.
    4. Henk F Moed, 2007. "The future of research evaluation rests with an intelligent combination of advanced metrics and transparent peer review," Science and Public Policy, Oxford University Press, vol. 34(8), pages 575-583, October.
    5. Giovanni Abramo & Ciriaco Andrea D'Angelo & Flavia Di Costa, 2010. "Testing the trade-off between productivity and quality in research activities," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(1), pages 132-140, January.
    6. Zhigang Hu & Chaomei Chen & Zeyuan Liu, 2014. "How are collaboration and productivity correlated at various career stages of scientists?," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1553-1564, November.
    7. Pablo Jensen & Jean-Baptiste Rouquier & Yves Croissant, 2009. "Testing bibliometric indicators by their prediction of scientists promotions," Scientometrics, Springer;Akadémiai Kiadó, vol. 78(3), pages 467-479, March.
    8. Rickard Danell, 2011. "Can the quality of scientific work be predicted using information on the author's track record?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(1), pages 50-60, January.
    9. Giovanni Abramo & Ciriaco Andrea D'Angelo & Flavia Di Costa, 2010. "Testing the trade‐off between productivity and quality in research activities," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(1), pages 132-140, January.
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    Cited by:

    1. Danielle H. Lee, 2019. "Predicting the research performance of early career scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1481-1504, December.
    2. Lindahl, Jonas, 2018. "Predicting research excellence at the individual level: The importance of publication rate, top journal publications, and top 10% publications in the case of early career mathematicians," Journal of Informetrics, Elsevier, vol. 12(2), pages 518-533.
    3. Deise Deolindo Silva & Maria Cláudia Cabrini Grácio, 2021. "Dispersion measures for h-index: a study of the Brazilian researchers in the field of mathematics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 1983-2011, March.
    4. Pär Sundling, 2023. "Author contributions and allocation of authorship credit: testing the validity of different counting methods in the field of chemical biology," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2737-2762, May.

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