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Bibliometric indicators of young authors in astrophysics: Can later stars be predicted?

Author

Listed:
  • Frank Havemann

    (Humboldt-Universität zu Berlin)

  • Birger Larsen

    (Aalborg University)

Abstract

We test 16 bibliometric indicators with respect to their validity at the level of the individual researcher by estimating their power to predict later successful researchers. We compare the indicators of a sample of astrophysics researchers who later co-authored highly cited papers before their first landmark paper with the distributions of these indicators over a random control group of young authors in astronomy and astrophysics. We find that field and citation-window normalisation substantially improves the predicting power of citation indicators. The sum of citation numbers normalised with expected citation numbers is the only indicator which shows differences between later stars and random authors significant on a 1 % level. Indicators of paper output are not very useful to predict later stars. The famous h-index makes no difference at all between later stars and the random control group.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:102:y:2015:i:2:d:10.1007_s11192-014-1476-3
    DOI: 10.1007/s11192-014-1476-3
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    3. Danielle H. Lee, 2019. "Predicting the research performance of early career scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1481-1504, December.
    4. 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.
    5. Cao, Xuanyu & Chen, Yan & Ray Liu, K.J., 2016. "A data analytic approach to quantifying scientific impact," Journal of Informetrics, Elsevier, vol. 10(2), pages 471-484.
    6. Dabić, Marina & Marzi, Giacomo & Vlačić, Božidar & Daim, Tugrul U. & Vanhaverbeke, Wim, 2021. "40 years of excellence: An overview of Technovation and a roadmap for future research," Technovation, Elsevier, vol. 106(C).
    7. Rodrigo Dorantes-Gilardi & Aurora A. Ramírez-Álvarez & Diana Terrazas-Santamaría, 2023. "Is there a differentiated gender effect of collaboration with super-cited authors? Evidence from junior researchers in economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2317-2336, April.
    8. Bin Wang & Feng Wu & Lukui Shi, 2023. "AGSTA-NET: adaptive graph spatiotemporal attention network for citation count prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 511-541, January.
    9. Cena, Anna & Gagolewski, Marek & Siudem, Grzegorz & Żogała-Siudem, Barbara, 2022. "Validating citation models by proxy indices," Journal of Informetrics, Elsevier, vol. 16(2).
    10. 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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