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Improved author profiling through the use of citation classes

Author

Listed:
  • Bart Thijs

    (KU Leuven)

  • Koenraad Debackere

    (KU Leuven)

  • Wolfgang Glänzel

    (KU Leuven
    Library of the Hungarian Academy of Sciences)

Abstract

The method of Characteristic Scores and Scales (CSS), previously developed for application at the macro- and meso-level, has been applied to individual author statistics. In particular, two datasets have been used. Firstly, authors with Thomson Reuters Researcher-ID, independently of the field where authors are publishing and, secondly, authors who are active in the field of scientometrics, independently whether they are registered authors or not. The objective is to find a parameter-free solution for citation-impact assessment at this level of aggregation that is insensitive to possible outliers. As in the case of any statistics, the only limitation is the lower bound, which has been set to 10 for the present analysis. The results demonstrate the usefulness of the CSS method at this level while also pointing to some remarkable statistical properties.

Suggested Citation

  • Bart Thijs & Koenraad Debackere & Wolfgang Glänzel, 2017. "Improved author profiling through the use of citation classes," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 829-839, May.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:2:d:10.1007_s11192-017-2282-5
    DOI: 10.1007/s11192-017-2282-5
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    References listed on IDEAS

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    1. Loet Leydesdorff & Lutz Bornmann & Rüdiger Mutz & Tobias Opthof, 2011. "Turning the tables on citation analysis one more time: Principles for comparing sets of documents," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(7), pages 1370-1381, July.
    2. Pedro Albarrán & Javier Ruiz‐Castillo, 2011. "References made and citations received by scientific articles," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(1), pages 40-49, January.
    3. Wolfgang Glänzel & Bart Thijs & András Schubert & Koenraad Debackere, 2009. "Subfield-specific normalized relative indicators and a new generation of relational charts: Methodological foundations illustrated on the assessment of institutional research performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 78(1), pages 165-188, January.
    4. Wildgaard, Lorna, 2016. "A critical cluster analysis of 44 indicators of author-level performance," Journal of Informetrics, Elsevier, vol. 10(4), pages 1055-1078.
    5. Wolfgang Glänzel & Bart Thijs & Koenraad Debackere, 2014. "The application of citation-based performance classes to the disciplinary and multidisciplinary assessment in national comparison and institutional research assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 939-952, November.
    6. Wolfgang Glänzel & András Schubert, 2003. "A new classification scheme of science fields and subfields designed for scientometric evaluation purposes," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(3), pages 357-367, March.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Ruiz-Castillo, Javier & Costas, Rodrigo, 2018. "Individual and field citation distributions in 29 broad scientific fields," Journal of Informetrics, Elsevier, vol. 12(3), pages 868-892.
    2. Wolfgang Glänzel & Bart Thijs, 2018. "The role of baseline granularity for benchmarking citation impact. The case of CSS profiles," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 521-536, July.
    3. David A. Pendlebury, 2019. "Charting a path between the simple and the false and the complex and unusable: Review of Henk F. Moed, Applied Evaluative Informetrics [in the series Qualitative and Quantitative Analysis of Scientifi," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 549-560, April.

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