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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

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  • Lindahl, Jonas

Abstract

The purpose of this study was to examine the relationship between publication rate, top journal publications and excellence during the first eight years of the career, and how well publication rate, top journal publications and highly cited publications during the first four years of the career can predict whether an author attain excellence in the fifth to the eighth year. The dataset consisted of publication track records of 406 early career mathematicians in the sub-field of number theory collected from the MathSciNet database. Logistic regression and dominance analysis was applied to the data. The major conclusions were (1) publication rate had a positive effect on excellence during the first eighth years of the career. However, those who publish many articles in top journals, which implicitly require a high publication count, had an even higher probability of attaining excellence. These results suggest that publishing in top journals is very important in the process of attaining excellence in the early career in addition to publishing many papers; and (2) a dominance analysis indicated that the number of top journal publications and highly cited publications during the first four years of the career were the most important predictors of who will attain excellence in the later career. The results are discussed in relation to indicator development and science policy.

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  • 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.
    • Handle: RePEc:eee:infome:v:12:y:2018:i:2:p:518-533
    DOI: 10.1016/j.joi.2018.04.002
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    1. Stephen J. Bensman & Lawrence J. Smolinsky & Alexander I. Pudovkin, 2010. "Mean citation rate per article in mathematics journals: Differences from the scientific model," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(7), pages 1440-1463, July.
    2. 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.
    3. Lutz Bornmann, 2014. "How are excellent (highly cited) papers defined in bibliometrics? A quantitative analysis of the literature," Research Evaluation, Oxford University Press, vol. 23(2), pages 166-173.
    4. Ana Ramos & Cláudia S. Sarrico, 2016. "Past performance does not guarantee future results: lessons from the evaluation of research units in Portugal," Research Evaluation, Oxford University Press, vol. 25(1), pages 94-106.
    5. George J. Borjas & Kirk B. Doran, 2021. "The Collapse Of The Soviet Union And The Productivity Of American Mathematicians," World Scientific Book Chapters, in: Foundational Essays in Immigration Economics, chapter 11, pages 313-373, World Scientific Publishing Co. Pte. Ltd..
    6. Tai-Quan Peng & Jonathan J.H. Zhu, 2012. "Where you publish matters most: A multilevel analysis of factors affecting citations of internet studies," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(9), pages 1789-1803, September.
    7. Lutz Bornmann & Rüdiger Mutz & Hans‐Dieter Daniel, 2008. "Are there better indices for evaluation purposes than the h index? A comparison of nine different variants of the h index using data from biomedicine," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(5), pages 830-837, March.
    8. 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.
    9. Fereshteh Didegah & Mike Thelwall, 2013. "Determinants of research citation impact in nanoscience and nanotechnology," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(5), pages 1055-1064, May.
    10. Lutz Bornmann, 2013. "How to analyze percentile citation impact data meaningfully in bibliometrics: The statistical analysis of distributions, percentile rank classes, and top-cited papers," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(3), pages 587-595, March.
    11. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
    12. Costas, Rodrigo & Bordons, María, 2007. "The h-index: Advantages, limitations and its relation with other bibliometric indicators at the micro level," Journal of Informetrics, Elsevier, vol. 1(3), pages 193-203.
    13. Franceschet, Massimo & Costantini, Antonio, 2010. "The effect of scholar collaboration on impact and quality of academic papers," Journal of Informetrics, Elsevier, vol. 4(4), pages 540-553.
    14. 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.
    15. Abramo, Giovanni & Cicero, Tindaro & D’Angelo, Ciriaco Andrea, 2014. "Are the authors of highly cited articles also the most productive ones?," Journal of Informetrics, Elsevier, vol. 8(1), pages 89-97.
    16. Bornmann, Lutz & Williams, Richard, 2017. "Can the journal impact factor be used as a criterion for the selection of junior researchers? A large-scale empirical study based on ResearcherID data," Journal of Informetrics, Elsevier, vol. 11(3), pages 788-799.
    17. 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.
    18. Olle Persson & Wolfgang Glänzel & Rickard Danell, 2004. "Inflationary bibliometric values: The role of scientific collaboration and the need for relative indicators in evaluative studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 60(3), pages 421-432, August.
    19. Stephen J. Bensman & Lawrence J. Smolinsky & Alexander I. Pudovkin, 2010. "Mean citation rate per article in mathematics journals: Differences from the scientific model," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(7), pages 1440-1463, July.
    20. McFadden, Daniel, 1974. "The measurement of urban travel demand," Journal of Public Economics, Elsevier, vol. 3(4), pages 303-328, November.
    21. Fereshteh Didegah & Mike Thelwall, 2013. "Determinants of research citation impact in nanoscience and nanotechnology," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(5), pages 1055-1064, May.
    22. Abramo, Giovanni & Cicero, Tindaro & D’Angelo, Ciriaco Andrea, 2011. "Assessing the varying level of impact measurement accuracy as a function of the citation window length," Journal of Informetrics, Elsevier, vol. 5(4), pages 659-667.
    23. 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.
    24. Razia Azen & Nicole Traxel, 2009. "Using Dominance Analysis to Determine Predictor Importance in Logistic Regression," Journal of Educational and Behavioral Statistics, , vol. 34(3), pages 319-347, September.
    25. Lawrence Smolinsky & Aaron Lercher, 2012. "Citation rates in mathematics: a study of variation by subdiscipline," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 911-924, June.
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    7. Jonas Lindahl & Cristian Colliander & Rickard Danell, 2020. "Early career performance and its correlation with gender and publication output during doctoral education," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 309-330, January.

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