IDEAS home Printed from https://ideas.repec.org/a/bla/jamist/v62y2011i1p50-60.html
   My bibliography  Save this article

Can the quality of scientific work be predicted using information on the author's track record?

Author

Listed:
  • Rickard Danell

Abstract

Many countries are moving towards research policies that emphasize excellence; consequently; they develop evaluation systems to identify universities, research groups, and researchers that can be said to be “excellent.” Such active research policy strategies, in which evaluations are used to concentrate resources, are based on an unsubstantiated assumption that researchers' track records are indicative of their future research performance. In this study, information on authors' track records (previous publication volume and previous citation rate) is used to predict the impact of their articles. The study concludes that, to a certain degree, the impact of scientific work can be predicted using information on how often an author's previous publications have been cited. The relationship between past performance and the citation rate of articles is strongest at the high end of the citation distribution. The implications of these results are discussed in the context of a cumulative advantage process.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jamist:v:62:y:2011:i:1:p:50-60
    DOI: 10.1002/asi.21454
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asi.21454
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asi.21454?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gen-Chang Hsu & Wei-Jiun Lin & Syuan-Jyun Sun, 2023. "Temporal trends in academic performance and career duration of principal investigators in ecology and evolutionary biology in Taiwan," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3437-3451, June.
    2. Petr Heneberg, 2013. "Lifting the fog of scientometric research artifacts: On the scientometric analysis of environmental tobacco smoke research," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(2), pages 334-344, February.
    3. Tehmina Amjad & Nafeesa Shahid & Ali Daud & Asma Khatoon, 2022. "Citation burst prediction in a bibliometric network," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2773-2790, May.
    4. Fenghua Wang & Ying Fan & An Zeng & Zengru Di, 2019. "Can we predict ESI highly cited publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 109-125, January.
    5. Bornmann, Lutz & Leydesdorff, Loet & Walch-Solimena, Christiane & Ettl, Christoph, 2011. "Mapping excellence in the geography of science: An approach based on Scopus data," Journal of Informetrics, Elsevier, vol. 5(4), pages 537-546.
    6. Vahid Garousi & João M. Fernandes, 2017. "Quantity versus impact of software engineering papers: a quantitative study," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 963-1006, August.
    7. 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.
    8. Mingyang Wang & Guang Yu & Shuang An & Daren Yu, 2012. "Discovery of factors influencing citation impact based on a soft fuzzy rough set model," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 635-644, December.
    9. Lutz Bornmann & Werner Marx, 2014. "How to evaluate individual researchers working in the natural and life sciences meaningfully? A proposal of methods based on percentiles of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 487-509, January.
    10. 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.
    11. 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.
    12. 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.
    13. Wang, Mingyang & Yu, Guang & Xu, Jianzhong & He, Huixin & Yu, Daren & An, Shuang, 2012. "Development a case-based classifier for predicting highly cited papers," Journal of Informetrics, Elsevier, vol. 6(4), pages 586-599.
    14. Peter Klimek & Aleksandar Jovanovic & Rainer Egloff & Reto Schneider, 2016. "Successful fish go with the flow: citation impact prediction based on centrality measures for term–document networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1265-1282, June.
    15. Li Hou & Qiang Wu & Yundong Xie, 2022. "Does early publishing in top journals really predict long-term scientific success in the business field?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6083-6107, November.
    16. Li, Xin & Wen, Yang & Jiang, Jiaojiao & Daim, Tugrul & Huang, Lucheng, 2022. "Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    17. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jamist:v:62:y:2011:i:1:p:50-60. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.asis.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.