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Selecting scientific excellence through committee peer review - A citation analysis of publications previously published to approval or rejection of post-doctoral research fellowship applicants

Author

Listed:
  • Lutz Bornmann

    (Swiss Federal Institute of Technology Zurich (ETH Zurich), Professorship for Social Psychology and Research on Higher Education)

  • Hans-Dieter Daniel

    (Swiss Federal Institute of Technology Zurich (ETH Zurich), Professorship for Social Psychology and Research on Higher Education; University of Zurich, Evaluation Office)

Abstract

Summary We investigated committee peer review for awarding long-term fellowships to post-doctoral researchers as practiced by the Boehringer Ingelheim Fonds (B.I.F.) - a foundation for the promotion of basic research in biomedicine. Assessing the validity of selection decisions requires a generally accepted criterion for research impact. A widely used approach is to use citation counts as a proxy for the impact of scientific research. Therefore, a citation analysis for articles published previous to the applicants' approval or rejection for a B.I.F. fellowship was conducted. Based on our model estimation (negative binomial regression model), journal articles that had been published by applicants approved for a fellowship award (n = 64) prior to applying for the B.I.F. fellowship award can be expected to have 37% (straight counts of citations) and 49% (complete counts of citations) more citations than articles that had been published by rejected applicants (n = 333). Furthermore, comparison with international scientific reference values revealed (a) that articles published by successful and non-successful applicants are cited considerably more often than the “average” publication and (b) that excellent research performance can be expected more of successful than non-successful applicants. The findings confirm that the foundation is not only achieving its goal of selecting the best junior scientists for fellowship awards, but also successfully attracting highly talented young scientists to apply for B.I.F. fellowships.

Suggested Citation

  • Lutz Bornmann & Hans-Dieter Daniel, 2006. "Selecting scientific excellence through committee peer review - A citation analysis of publications previously published to approval or rejection of post-doctoral research fellowship applicants," Scientometrics, Springer;Akadémiai Kiadó, vol. 68(3), pages 427-440, September.
    • Handle: RePEc:spr:scient:v:68:y:2006:i:3:d:10.1007_s11192-006-0121-1
    DOI: 10.1007/s11192-006-0121-1
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    Citations

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    Cited by:

    1. Alexandre Rodrigues Oliveira & Carlos Fernando Mello, 2016. "Importance and susceptibility of scientific productivity indicators: two sides of the same coin," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 697-722, November.
    2. Kaur, Jasleen & Radicchi, Filippo & Menczer, Filippo, 2013. "Universality of scholarly impact metrics," Journal of Informetrics, Elsevier, vol. 7(4), pages 924-932.
    3. Primož Južnič & Stojan Pečlin & Matjaž Žaucer & Tilen Mandelj & Miro Pušnik & Franci Demšar, 2010. "Scientometric indicators: peer-review, bibliometric methods and conflict of interests," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(2), pages 429-441, November.
    4. Benda, Wim G.G. & Engels, Tim C.E., 2011. "The predictive validity of peer review: A selective review of the judgmental forecasting qualities of peers, and implications for innovation in science," International Journal of Forecasting, Elsevier, vol. 27(1), pages 166-182.
    5. Bornmann, Lutz & Schier, Hermann & Marx, Werner & Daniel, Hans-Dieter, 2012. "What factors determine citation counts of publications in chemistry besides their quality?," Journal of Informetrics, Elsevier, vol. 6(1), pages 11-18.
    6. Yan Yan & Shanwu Tian & Jingjing Zhang, 2020. "The impact of a paper’s new combinations and new components on its citation," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 895-913, February.
    7. Bornmann, Lutz & Mutz, Rüdiger & Daniel, Hans-Dieter, 2008. "Latent Markov modeling applied to grant peer review," Journal of Informetrics, Elsevier, vol. 2(3), pages 217-228.
    8. Guoqiang Liang & Haiyan Hou & Xiaodan Lou & Zhigang Hu, 2019. "Qualifying threshold of “take-off” stage for successfully disseminated creative ideas," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1193-1208, September.
    9. Kevin W. Boyack & Caleb Smith & Richard Klavans, 2018. "Toward predicting research proposal success," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 449-461, February.
    10. Li, Jiang & Sanderson, Mark & Willett, Peter & Norris, Michael & Oppenheim, Charles, 2010. "Ranking of library and information science researchers: Comparison of data sources for correlating citation data, and expert judgments," Journal of Informetrics, Elsevier, vol. 4(4), pages 554-563.
    11. van den Besselaar, Peter & Sandström, Ulf, 2015. "Early career grants, performance, and careers: A study on predictive validity of grant decisions," Journal of Informetrics, Elsevier, vol. 9(4), pages 826-838.
    12. Hans-Dieter Daniel, 2019. "Lutz Bornmann: Recipient of the 2019 Derek John de Solla Price Medal," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1235-1238, December.
    13. Vieira, Elizabeth S. & Cabral, José A.S. & Gomes, José A.N.F., 2014. "How good is a model based on bibliometric indicators in predicting the final decisions made by peers?," Journal of Informetrics, Elsevier, vol. 8(2), pages 390-405.
    14. Bornmann, Lutz & Daniel, Hans-Dieter, 2007. "Gatekeepers of science—Effects of external reviewers’ attributes on the assessments of fellowship applications," Journal of Informetrics, Elsevier, vol. 1(1), pages 83-91.
    15. Benda, Wim G.G. & Engels, Tim C.E., 2011. "The predictive validity of peer review: A selective review of the judgmental forecasting qualities of peers, and implications for innovation in science," International Journal of Forecasting, Elsevier, vol. 27(1), pages 166-182, January.
    16. Andrea Bonaccorsi & Luca Secondi, 2017. "The determinants of research performance in European universities: a large scale multilevel analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1147-1178, September.
    17. Filippo Radicchi & Claudio Castellano, 2013. "Analysis of bibliometric indicators for individual scholars in a large data set," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 627-637, December.
    18. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
    19. Radicchi, Filippo & Castellano, Claudio, 2012. "Testing the fairness of citation indicators for comparison across scientific domains: The case of fractional citation counts," Journal of Informetrics, Elsevier, vol. 6(1), pages 121-130.
    20. Vieira, E.S. & Gomes, J.A.N.F., 2010. "Citations to scientific articles: Its distribution and dependence on the article features," Journal of Informetrics, Elsevier, vol. 4(1), pages 1-13.
    21. Stefan Hornbostel & Susan Böhmer & Bernd Klingsporn & Jörg Neufeld & Markus Ins, 2009. "Funding of young scientist and scientific excellence," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(1), pages 171-190, April.
    22. Bornmann, Lutz & Daniel, Hans-Dieter, 2007. "Convergent validation of peer review decisions using the h index," Journal of Informetrics, Elsevier, vol. 1(3), pages 204-213.
    23. Radicchi, Filippo & Weissman, Alexander & Bollen, Johan, 2017. "Quantifying perceived impact of scientific publications," Journal of Informetrics, Elsevier, vol. 11(3), pages 704-712.
    24. Zhiya Zuo & Kang Zhao, 2021. "Understanding and predicting future research impact at different career stages—A social network perspective," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(4), pages 454-472, April.
    25. Kaur, Jasleen & Ferrara, Emilio & Menczer, Filippo & Flammini, Alessandro & Radicchi, Filippo, 2015. "Quality versus quantity in scientific impact," Journal of Informetrics, Elsevier, vol. 9(4), pages 800-808.

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