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A comparison of the Web of Science and publication-level classification systems of science

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  • Perianes-Rodriguez, Antonio
  • Ruiz-Castillo, Javier

Abstract

In this paper, we propose a new criterion for choosing between a pair of classification systems of science that assign publications (or journals) to a set of clusters. Consider the standard target (cited-side) normalization procedure in which cluster mean citations are used as normalization factors. We recommend system A over system B whenever the standard normalization procedure based on system A performs better than the standard normalization procedure based on system B. Performance is assessed in terms of two double tests – one graphical, and one numerical – that use both classification systems for evaluation purposes. In addition, a pair of classification systems is compared using a third, independent classification system for evaluation purposes. We illustrate this strategy by comparing a Web of Science journal-level classification system, consisting of 236 journal subject categories, with two publication-level algorithmically constructed classification systems consisting of 1363 and 5119 clusters. There are two main findings. Firstly, the second publication-level system is found to dominate the first. Secondly, the publication-level system at the highest granularity level and the Web of Science journal-level system are found to be non-comparable. Nevertheless, we find reasons to recommend the publication-level option.

Suggested Citation

  • Perianes-Rodriguez, Antonio & Ruiz-Castillo, Javier, 2017. "A comparison of the Web of Science and publication-level classification systems of science," Journal of Informetrics, Elsevier, vol. 11(1), pages 32-45.
    • Handle: RePEc:eee:infome:v:11:y:2017:i:1:p:32-45
    DOI: 10.1016/j.joi.2016.10.007
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    References listed on IDEAS

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    1. Li, Yunrong & Ruiz-Castillo, Javier, 2013. "The comparison of normalization procedures based on different classification systems," Journal of Informetrics, Elsevier, vol. 7(4), pages 945-958.
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    Cited by:

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    3. Gabriel-Alexandru Vîiu & Mihai Păunescu, 2021. "The citation impact of articles from which authors gained monetary rewards based on journal metrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4941-4974, June.
    4. Carusi, Chiara & Bianchi, Giuseppe, 2019. "Scientific community detection via bipartite scholar/journal graph co-clustering," Journal of Informetrics, Elsevier, vol. 13(1), pages 354-386.
    5. Juan Miguel Campanario, 2018. "Are leaders really leading? Journals that are first in Web of Science subject categories in the context of their groups," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 111-130, April.
    6. Sjögårde, Peter & Ahlgren, Per, 2018. "Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics," Journal of Informetrics, Elsevier, vol. 12(1), pages 133-152.
    7. Mike Thelwall, 2019. "The influence of highly cited papers on field normalised indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(2), pages 519-537, February.
    8. Berndt Jesenko & Christian Schlögl, 2021. "The effect of web of science subject categories on clustering: the case of data-driven methods in business and economic sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6785-6801, August.
    9. Sitaram Devarakonda & Dmitriy Korobskiy & Tandy Warnow & George Chacko, 2020. "Viewing computer science through citation analysis: Salton and Bergmark Redux," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 271-287, October.
    10. Bornmann, Lutz & Marx, Werner, 2018. "Critical rationalism and the search for standard (field-normalized) indicators in bibliometrics," Journal of Informetrics, Elsevier, vol. 12(3), pages 598-604.
    11. Shir Aviv-Reuven & Ariel Rosenfeld, 2023. "A logical set theory approach to journal subject classification analysis: intra-system irregularities and inter-system discrepancies in Web of Science and Scopus," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 157-175, January.

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