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The role of baseline granularity for benchmarking citation impact. The case of CSS profiles

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  • Wolfgang Glänzel

    (KU Leuven
    Library of the Hungarian Academy of Sciences)

  • Bart Thijs

    (KU Leuven)

Abstract

In this paper we study the effect of granularity on Characteristic Scores and Scales (CSS). Unlike the traditional indicators that are mostly based on means and quantiles, CSS require the reduction of the citation distributions collaboration of the underlying reference population to four states (classes) and thus higher a different level of granularity. While the question of the choice of granularity is at higher levels of aggregation usually not critical since countries and university have rather multidisciplinary profiles, at lower aggregation levels specialisation becomes more typical. Inappropriate granularity might not warrant the depiction of the publication profiles at these levels in a correct and adequate manner and thus not add accurate citation profiles either. In order to be able to process one complete annual volume of the Web of Science, we decided to calculate CSS thresholds and classes for two levels of granularity, namely sub-fields and WoS Subject Categories. With about 5% deviation, we did not find a real significance. However, we identified journals with similar impact measures but different citation profiles, independently of the granularity. Finally, we have pointed to the limitations in the choice of granularity—in terms of both too broad and too narrow subjects.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:116:y:2018:i:1:d:10.1007_s11192-018-2747-1
    DOI: 10.1007/s11192-018-2747-1
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    References listed on IDEAS

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    1. 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.
    2. Wolfgang Glänzel & Henk F. Moed, 2002. "Journal impact measures in bibliometric research," Scientometrics, Springer;Akadémiai Kiadó, vol. 53(2), pages 171-193, February.
    3. Jonathan Adams & Karen Gurney & Louise Jackson, 2008. "Calibrating the zoom — a test of Zitt’s hypothesis," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(1), pages 81-95, April.
    4. 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.
    5. 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.
    6. Michel Zitt & Suzy Ramanana-Rahary & Elise Bassecoulard, 2005. "Relativity of citation performance and excellence measures: From cross-field to cross-scale effects of field-normalisation," Scientometrics, Springer;Akadémiai Kiadó, vol. 63(2), pages 373-401, April.
    7. 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.
    8. 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.
    9. Wolfgang Glänzel & Ping Zhou, 2011. "Publication activity, citation impact and bi-directional links between publications and patents in biotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(2), pages 505-525, February.
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    Cited by:

    1. Mingyang Wang & Jiaqi Zhang & Shijia Jiao & Xiangrong Zhang & Na Zhu & Guangsheng Chen, 2020. "Important citation identification by exploiting the syntactic and contextual information of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2109-2129, December.
    2. Mingyang Wang & Jiaqi Zhang & Shijia Jiao & Tianyu Zhang, 2019. "Evaluating the impact of citations of articles based on knowledge flow patterns hidden in the citations," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-19, November.
    3. Wolfgang Glänzel & Koenraad Debackere, 2022. "Various aspects of interdisciplinarity in research and how to quantify and measure those," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5551-5569, September.
    4. Mingyang Wang & Shijia Jiao & Kah-Hin Chai & Guangsheng Chen, 2019. "Building journal’s long-term impact: using indicators detected from the sustained active articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 261-283, October.

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