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Ranking journals using social choice theory methods: A novel approach in bibliometrics

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  • Subochev, Andrey
  • Aleskerov, Fuad
  • Pislyakov, Vladimir

Abstract

We use data on economic, management and political science journals to produce quantitative estimates of (in)consistency of the evaluations based on six popular bibliometric indicators (impact factor, 5-year impact factor, immediacy index, article influence score, SNIP and SJR). We advocate a new approach to the aggregation of journal rankings. Since the rank aggregation is a multicriteria decision problem, ranking methods from social choice theory may solve it. We apply either a direct ranking method based on the majority rule (the Copeland rule, the Markovian method) or a sorting procedure based on a tournament solution, such as the uncovered set and the minimal externally stable set. We demonstrate that the aggregate rankings reduce the number of contradictions and represent the set of the single-indicator-based rankings better than any of the six rankings themselves.

Suggested Citation

  • Subochev, Andrey & Aleskerov, Fuad & Pislyakov, Vladimir, 2018. "Ranking journals using social choice theory methods: A novel approach in bibliometrics," Journal of Informetrics, Elsevier, vol. 12(2), pages 416-429.
    • Handle: RePEc:eee:infome:v:12:y:2018:i:2:p:416-429
    DOI: 10.1016/j.joi.2018.03.001
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    Cited by:

    1. Tatiana Lisitskaya & Pavel Taranov & Ekaterina Ugnich & Vladimir Pislyakov, 2024. "Pillar Universities in Russia: Bibliometrics of ‘the second best’," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 365-383, February.
    2. Saarela, Mirka & Kärkkäinen, Tommi, 2020. "Can we automate expert-based journal rankings? Analysis of the Finnish publication indicator," Journal of Informetrics, Elsevier, vol. 14(2).
    3. Fernandez Martinez, Roberto & Lostado Lorza, Ruben & Santos Delgado, Ana Alexandra & Piedra, Nelson, 2021. "Use of classification trees and rule-based models to optimize the funding assignment to research projects: A case study of UTPL," Journal of Informetrics, Elsevier, vol. 15(1).
    4. Mark Levene & Trevor Fenner & Judit Bar-Ilan, 2019. "Characterisation of the $$\chi$$χ-index and the rec-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 885-896, August.
    5. Aleksei Y. Kondratev & Vladimir V. Mazalov, 2020. "Tournament solutions based on cooperative game theory," International Journal of Game Theory, Springer;Game Theory Society, vol. 49(1), pages 119-145, March.
    6. Bianchi, Federico & Grimaldo, Francisco & Squazzoni, Flaminio, 2019. "The F3-index. Valuing reviewers for scholarly journals," Journal of Informetrics, Elsevier, vol. 13(1), pages 78-86.
    7. Pislyakov, Vladimir, 2022. "On some properties of medians, percentiles, baselines, and thresholds in empirical bibliometric analysis," Journal of Informetrics, Elsevier, vol. 16(4).
    8. 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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