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Sub-field normalization in the multiplicative case : high- and low- impact citation indicators

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  • Herranz, Neus
  • Ruiz-Castillo, Javier

Abstract

This paper uses high- and low-impact citation indicators for the evaluation of the citation performance of research units at different aggregate levels. To solve the problem of the assignment of individual articles to multiple sub-fields, it follows a multiplicative strategy according to which each paper is wholly counted as many times as necessary in the several categories to which it is assigned at each aggregation level. To control for wide differences in citation practices at the lowest level of aggregation, we apply a novel sub-field normalization procedure in the multiplicative case. The methodology is applied to a partition of the world into three geographical areas: the U.S., the European Union (EU), and the Rest of the World. The main findings are the following two. (i) Although normalization does not systematically bias the results against any area, it reduces the U.S./EU highimpact gap in the all-sciences case by a non-negligible 14.4%. (ii) The dominance of the U.S. over the EU in the basic and applied research published in the periodical literature is almost universal at all aggregation levels. From the high-impact perspective, for example, the U.S. is ahead of the EU in 77 out of 80 disciplines, and all of 20 fields. For all sciences as a whole, the U.S. high-impact indicator is 61% greater than that of the EU.

Suggested Citation

  • Herranz, Neus & Ruiz-Castillo, Javier, 2011. "Sub-field normalization in the multiplicative case : high- and low- impact citation indicators," UC3M Working papers. Economics we1131, Universidad Carlos III de Madrid. Departamento de Economía.
  • Handle: RePEc:cte:werepe:we1131
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    References listed on IDEAS

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    1. Pedro Albarrán & Juan A. Crespo & Ignacio Ortuño & Javier Ruiz-Castillo, 2011. "The skewness of science in 219 sub-fields and a number of aggregates," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 385-397, August.
    2. Albarrán, Pedro & Ortuño, Ignacio & Ruiz-Castillo, Javier, 2011. "The measurement of low- and high-impact in citation distributions: Technical results," Journal of Informetrics, Elsevier, vol. 5(1), pages 48-63.
    3. Per O. Seglen, 1992. "The skewness of science," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 43(9), pages 628-638, October.
    4. 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.
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    1. Herranz, Neus & Ruiz-Castillo, Javier, 2012. "Sub-field normalization in the multiplicative case: Average-based citation indicators," Journal of Informetrics, Elsevier, vol. 6(4), pages 543-556.
    2. Albarrán, Pedro & Herrero, Carmen & Ruiz-Castillo, Javier & Villar, Antonio, 2017. "The Herrero-Villar approach to citation impact," Journal of Informetrics, Elsevier, vol. 11(2), pages 625-640.
    3. Finardi, Ugo, 2013. "Correlation between Journal Impact Factor and Citation Performance: An experimental study," Journal of Informetrics, Elsevier, vol. 7(2), pages 357-370.
    4. Guillermo Armando Ronda-Pupo, 2020. "The performance of Latin American research on economics & business," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 573-590, January.
    5. Ruiz-Castillo, Javier & Costas, Rodrigo, 2014. "The skewness of scientific productivity," Journal of Informetrics, Elsevier, vol. 8(4), pages 917-934.
    6. Franceschini, Fiorenzo & Maisano, Domenico, 2014. "Sub-field normalization of the IEEE scientific journals based on their connection with Technical Societies," Journal of Informetrics, Elsevier, vol. 8(3), pages 508-533.
    7. Yunrong Li & Javier Ruiz-Castillo, 2014. "The impact of extreme observations in citation distributions," Research Evaluation, Oxford University Press, vol. 23(2), pages 174-182.
    8. Tol, Richard S.J., 2013. "Identifying excellent researchers: A new approach," Journal of Informetrics, Elsevier, vol. 7(4), pages 803-810.
    9. Maximiano Ortiz-Pimentel & Carlos Molina & Guillermo Armando Ronda-Pupo, 2020. "Bibliometric assessment of papers on generations in management and business journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 445-469, October.
    10. Waltman, Ludo & van Eck, Nees Jan, 2015. "Field-normalized citation impact indicators and the choice of an appropriate counting method," Journal of Informetrics, Elsevier, vol. 9(4), pages 872-894.
    11. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2019. "On the interplay between normalisation, bias, and performance of paper impact metrics," Journal of Informetrics, Elsevier, vol. 13(1), pages 270-290.

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    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • Y80 - Miscellaneous Categories - - Related Disciplines - - - Related Disciplines
    • Z00 - Other Special Topics - - General - - - General

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