IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v17y2023i4s1751157723000901.html
   My bibliography  Save this article

Empirical demonstration of the Matthew effect in scientific research careers

Author

Listed:
  • Katchanov, Yurij L.
  • Markova, Yulia V.
  • Shmatko, Natalia A.

Abstract

The Matthew effect qualitatively describes the social phenomenon that the impact and recognition of well-known scientists for their new accomplishments are relatively overpriced by the scientific community when compared to the similar output of researchers without fame or status. We quantify the manifestation of this phenomenon in scientific research careers. For this purpose, using a mixed survey-plus-bibliometrics method, we assembled a dataset containing detailed career information on scientists in the field of chemistry. The mathematical model of the Matthew effect in scientific research careers proposed in this paper identifies career distribution as the generalized extreme value distribution with the shape parameter q=0.5. This result is in good agreement with the obtained data: the empirical distribution of scientific careers can be approximated by the generalized extreme value distribution with q=0.423. We also find that the distribution of starting positions of social trajectories of scientists fits by the Pareto distribution. Our analysis deepens scientific insight into the emergence of the Matthew effect in scientific careers and its relationship to the distribution of citations and citation entropy.

Suggested Citation

  • Katchanov, Yurij L. & Markova, Yulia V. & Shmatko, Natalia A., 2023. "Empirical demonstration of the Matthew effect in scientific research careers," Journal of Informetrics, Elsevier, vol. 17(4).
  • Handle: RePEc:eee:infome:v:17:y:2023:i:4:s1751157723000901
    DOI: 10.1016/j.joi.2023.101465
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157723000901
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.joi.2023.101465?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:infome:v:17:y:2023:i:4:s1751157723000901. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    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.