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Improved author profiling through the use of citation classes

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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.

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Acknowledgement

The present study is an extended version of an article presented at the Proceedings of the Science and Technology Indicators Conference 2014 Leiden, 3–5 September 2014 (Thijs et al. 2014). We would like to thank the anonymous reviewers at different stages of this study for their valuable suggestions and remarks.

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Authors and Affiliations

  1. Centre for R&D Monitoring (ECOOM) and Dept. MSI, KU Leuven, Louvain, Belgium

    Bart Thijs, Koenraad Debackere & Wolfgang Glänzel

  2. Department of Science Policy & Scientometrics, Library of the Hungarian Academy of Sciences, Budapest, Hungary

    Wolfgang Glänzel

Authors
  1. Bart Thijs
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  2. Koenraad Debackere
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  3. Wolfgang Glänzel
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Correspondence to Bart Thijs.

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Thijs, B., Debackere, K. & Glänzel, W. Improved author profiling through the use of citation classes. Scientometrics 111, 829–839 (2017). https://doi.org/10.1007/s11192-017-2282-5

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