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Testing bibliometric indicators by their prediction of scientists promotions

Author

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  • Pablo Jensen

    (Université de Lyon
    Institut des Systèmes Complexes Rhône-Alpes (IXXI)
    École Normale Supérieure de Lyon and CNRS
    Université Lyon 2 and CNRS)

  • Jean-Baptiste Rouquier

    (Université de Lyon
    Institut des Systèmes Complexes Rhône-Alpes (IXXI)
    École Normale Supérieure de Lyon and CNRS)

  • Yves Croissant

    (Université de Lyon
    Université Lyon 2 and CNRS)

Abstract

We have developed a method to obtain robust quantitative bibliometric indicators for several thousand scientists. This allows us to study the dependence of bibliometric indicators (such as number of publications, number of citations, Hirsch index...) on the age, position, etc. of CNRS scientists. Our data suggests that the normalized h-index (h divided by the career length) is not constant for scientists with the same productivity but different ages. We also compare the predictions of several bibliometric indicators on the promotions of about 600 CNRS researchers. Contrary to previous publications, our study encompasses most disciplines, and shows that no single indicator is the best predictor for all disciplines. Overall, however, the Hirsch index h provides the least bad correlations, followed by the number of papers published. It is important to realize however that even h is able to recover only half of the actual promotions. The number of citations or the mean number of citations per paper are definitely not good predictors of promotion. Due to space constraints, this paper is a short version of a more detailed article. [JENSEN & AL., 2008B]

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:78:y:2009:i:3:d:10.1007_s11192-007-2014-3
    DOI: 10.1007/s11192-007-2014-3
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    Cited by:

    1. Marcel Clermont & Alexander Dirksen & Barbara Scheidt & Dirk Tunger, 2017. "Citation metrics as an additional indicator for evaluating research performance? An analysis of their correlations and validity," Business Research, Springer;German Academic Association for Business Research, vol. 10(2), pages 249-279, October.
    2. Marcel Clermont & Johanna Krolak & Dirk Tunger, 2021. "Does the citation period have any effect on the informative value of selected citation indicators in research evaluations?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1019-1047, February.
    3. Marine Bernard & Bastien Bernela & Marie Ferru, 2021. "Does the geographical mobility of scientists shape their collaboration network? A panel approach of chemists’ careers," Papers in Regional Science, Wiley Blackwell, vol. 100(1), pages 79-99, February.
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    5. Xingchen Li & Qiang Wu & Yuanyuan Liu, 2017. "A quantitative analysis of researcher citation personal display considering disciplinary differences and influence factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 1093-1112, November.
    6. Glenn Ellison, 2013. "How Does the Market Use Citation Data? The Hirsch Index in Economics," American Economic Journal: Applied Economics, American Economic Association, vol. 5(3), pages 63-90, July.
    7. Giovanni Abramo & Ciriaco Andrea D’Angelo & Fulvio Viel, 2013. "The suitability of h and g indexes for measuring the research performance of institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 555-570, December.
    8. Peep Küngas & Siim Karus & Svitlana Vakulenko & Marlon Dumas & Cristhian Parra & Fabio Casati, 2013. "Reverse-engineering conference rankings: what does it take to make a reputable conference?," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(2), pages 651-665, August.
    9. Danielle H. Lee, 2019. "Predicting the research performance of early career scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1481-1504, December.
    10. 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.
    11. Kwon, Eunrang & Yun, Jinhyuk & Kang, Jeong-han, 2023. "The effect of the COVID-19 pandemic on gendered research productivity and its correlates," Journal of Informetrics, Elsevier, vol. 17(1).
    12. Jakub Rybacki & Dobromił Serwa, 2021. "What Makes a Successful Scientist in a Central Bank? Evidence From the RePEc Database," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 13(3), pages 331-357, September.
    13. Vicente Prado-Gascó & María del Carmen Giménez-Espert & Hans De Witte, 2021. "Job Insecurity in Nursing: A Bibliometric Analysis," IJERPH, MDPI, vol. 18(2), pages 1-13, January.
    14. Sebastian Hager & Carlo Schwarz & Fabian Waldinger, 2023. "Measuring Science: Performance Metrics and the Allocation of Talent," Rationality and Competition Discussion Paper Series 455, CRC TRR 190 Rationality and Competition.
    15. Victor Tiberius & Meike Rietz & Ricarda B. Bouncken, 2020. "Performance Analysis and Science Mapping of Institutional Entrepreneurship Research," Administrative Sciences, MDPI, vol. 10(3), pages 1-21, September.
    16. Andrea Giovanni Nuzzolese & Paolo Ciancarini & Aldo Gangemi & Silvio Peroni & Francesco Poggi & Valentina Presutti, 2019. "Do altmetrics work for assessing research quality?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(2), pages 539-562, February.
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    18. Lin Zhang & Wolfgang Glänzel, 2012. "Where demographics meets scientometrics: towards a dynamic career analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 617-630, May.

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