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The big challenge of Scientometrics 2.0: exploring the broader impact of scientific research in public health

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  • Wolfgang Glänzel

    (KU Leuven
    Library of the Hungarian Academy of Sciences)

  • Pei-Shan Chi

    (KU Leuven)

Abstract

In the present study we discuss the challenge of “Scientometrics 2.0” as introduced by Priem and Hemminger (2010) in the light of possible applications to research evaluation. We use the Web of Science subject category public, environmental and occupational health to illustrate how indicators similar to those used in traditional scientometrics can be built, and we also discuss their opportunities and limitations. The discipline under study combines life sciences and social sciences in a unique manner and provides usable metrics reflecting both scholarly and wider impact. Nonetheless, metrics reflecting social media attention like tweets, retweets and Facebook likes, shares or comments are still subject to limitations in this research discipline as well. Furthermore, Usage metrics clearly point to the manipulation proneness of this measure. Although the counterparts of important bibliometric indicators proved to work for several altmetrics too, their interpretation and application to research assessment requires proper context analysis.

Suggested Citation

  • Wolfgang Glänzel & Pei-Shan Chi, 2020. "The big challenge of Scientometrics 2.0: exploring the broader impact of scientific research in public health," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1011-1031, November.
  • Handle: RePEc:spr:scient:v:125:y:2020:i:2:d:10.1007_s11192-020-03473-x
    DOI: 10.1007/s11192-020-03473-x
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    Cited by:

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    4. Zhiqi Wang & Wolfgang Glänzel & Yue Chen, 2020. "The impact of preprints in Library and Information Science: an analysis of citations, usage and social attention indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1403-1423, November.
    5. Steve O’Hern & Nora Estgfaeller, 2020. "A Scientometric Review of Powered Micromobility," Sustainability, MDPI, vol. 12(22), pages 1-21, November.
    6. Qianjin Zong & Zhihong Huang & Jiaru Huang, 2023. "Can open access increase LIS research’s policy impact? Using regression analysis and causal inference," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4825-4854, August.

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