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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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References

  • Abdulhayoglu, M.A. & Thijs, B. (2016). Leveraging researchgate for the purpose of author name disambiguation. GTM2016: 6th Global Techmining Conference. Valencia, Spain.

  • Albarrán, P., & Ruiz-Castillo, J. (2011). References made and citations received by scientific articles. Journal of the Association for Information Science and Technology, 62(1), 40–49.

    Article  Google Scholar 

  • Glänzel, W. (2007). Characteristic scores and scales. A bibliometric analysis of subject characteristics based on long-term citation observation. Journal of Informetrics, 1(1), 92–102.

    Article  Google Scholar 

  • Glänzel, W. (2013). High-end performance or outlier? Evaluating the tail of scientometric distribution. Scientometrics, 97(1), 13–23.

    Article  Google Scholar 

  • Glänzel, W., & Schubert, A. (1988). Characteristic scores and scales in assessing citation impact. Journal of Information Science, 14(2), 123–127.

    Article  Google Scholar 

  • Glänzel, W., & Schubert, A. (2003). A new classification scheme of science fields and subfields designed for scientometric evaluation purposes. Scientometrics, 56(3), 357–367.

    Article  Google Scholar 

  • Glänzel, W., Schubert, A., Thijs, B., & Debackere, K. (2009). Subfield-specific normalized relative indicators and a new generation of relational charts: Methodological foundations illustrated on the assessment of institutional research performance. Scientometrics, 78(1), 165–188.

    Article  Google Scholar 

  • Glänzel, W., Thijs, B., & Debackere, K. (2014). The application of citation-based performance classes to the disciplinary and multidisciplinary assessment in national comparison and institutional research assessment. Scientometrics. doi:10.1007/s11192-014-1247-1. (in press).

    Google Scholar 

  • Heeffer, S., Thijs, B., & Glänzel, W. (2013). Are registered authors more productive? ISSI Newsletter, 9(2), 29–32.

    Google Scholar 

  • Leydesdorff, L., Bornmann, L., Mutz, R., & Opthof, T. (2011). Turning the tables on citation analysis one more time: Principles for comparing sets of documents. Journal of the Association for Information Science and Technology, 62(7), 1370–1381.

    Article  Google Scholar 

  • Thijs, B., Debackere, K., Glänzel, W. (2014), Improved author profiling through the use of citation classes. In: Ed Noyons (Ed.), Proceedings of the science and technology indicators conference 2014 Leiden, “Context Counts: Pathways to Master Big and Little Data”, held on 3–5 September 2014 in Leiden, the Netherlands, University Leiden, 616–622.

  • Wildgaard, L. (2016). A critical analysis of 44 indicators of author-level performance. Journal on Informetrics, 10(4), 1055–1078.

    Article  Google Scholar 

  • Wildgaard, L., Schneider, J. W., & Larsen, B. (2014). A review of the characteristics of 108 author-level biliometric indicators. Scientometrics, 101(1), 125–158.

    Article  Google Scholar 

Download references

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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Corresponding author

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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  • DOI: https://doi.org/10.1007/s11192-017-2282-5

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