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An investigation on the skewness patterns and fractal nature of research productivity distributions at field and discipline level

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  • Abramo, Giovanni
  • D’Angelo, Ciriaco Andrea
  • Soldatenkova, Anastasiia

Abstract

The paper provides an empirical examination of how research productivity distributions differ across scientific fields and disciplines. Productivity is measured using the FSS indicator, which embeds both quantity and impact of output. The population studied consists of over 31,000 scientists in 180 fields (10 aggregate disciplines) of a national research system. The Characteristic Scores and Scale technique is used to investigate the distribution patterns for the different fields and disciplines. Research productivity distributions are found to be asymmetrical at the field level, although the degree of skewness varies substantially among the fields within the aggregate disciplines. We also examine whether the field productivity distributions show a fractal nature, which reveals an exception more than a rule. Differently, for the disciplines, the partitions of the distributions show skewed patterns that are highly similar.

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  • Abramo, Giovanni & D’Angelo, Ciriaco Andrea & Soldatenkova, Anastasiia, 2017. "An investigation on the skewness patterns and fractal nature of research productivity distributions at field and discipline level," Journal of Informetrics, Elsevier, vol. 11(1), pages 324-335.
    • Handle: RePEc:eee:infome:v:11:y:2017:i:1:p:324-335
    DOI: 10.1016/j.joi.2017.02.001
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    References listed on IDEAS

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    1. Abramo, Giovanni & D’Angelo, Ciriaco Andrea, 2016. "A farewell to the MNCS and like size-independent indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 646-651.
    2. Lutz Bornmann & Wolfgang Glänzel, 2017. "Applying the CSS method to bibliometric indicators used in (university) rankings," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 1077-1079, February.
    3. 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.
    4. Bornmann, Lutz & Leydesdorff, Loet, 2017. "Skewness of citation impact data and covariates of citation distributions: A large-scale empirical analysis based on Web of Science data," Journal of Informetrics, Elsevier, vol. 11(1), pages 164-175.
    5. Arnab Chatterjee & Asim Ghosh & Bikas K Chakrabarti, 2016. "Universality of Citation Distributions for Academic Institutions and Journals," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-11, January.
    6. Ciriaco Andrea D'Angelo & Cristiano Giuffrida & Giovanni Abramo, 2011. "A heuristic approach to author name disambiguation in bibliometrics databases for large-scale research assessments," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(2), pages 257-269, February.
    7. Ciriaco Andrea D'Angelo & Cristiano Giuffrida & Giovanni Abramo, 2011. "A heuristic approach to author name disambiguation in bibliometrics databases for large‐scale research assessments," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(2), pages 257-269, February.
    8. Javier Ruiz-Castillo, 2012. "The evaluation of citation distributions," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(1), pages 291-310, March.
    9. Abramo, Giovanni & Cicero, Tindaro & D’Angelo, Ciriaco Andrea, 2012. "Revisiting the scaling of citations for research assessment," Journal of Informetrics, Elsevier, vol. 6(4), pages 470-479.
    10. Ruiz-Castillo, Javier & Costas, Rodrigo, 2014. "The skewness of scientific productivity," Journal of Informetrics, Elsevier, vol. 8(4), pages 917-934.
    11. Abramo, Giovanni & D’Angelo, Ciriaco Andrea & Rosati, Francesco, 2013. "The importance of accounting for the number of co-authors and their order when assessing research performance at the individual level in the life sciences," Journal of Informetrics, Elsevier, vol. 7(1), pages 198-208.
    12. 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.
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    Cited by:

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    2. Vîiu, Gabriel-Alexandru, 2018. "The lognormal distribution explains the remarkable pattern documented by characteristic scores and scales in scientometrics," Journal of Informetrics, Elsevier, vol. 12(2), pages 401-415.
    3. Gerson Pech & Catarina Delgado, 2020. "Percentile and stochastic-based approach to the comparison of the number of citations of articles indexed in different bibliographic databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 223-252, April.
    4. Cinelli, Matteo, 2021. "Ambiguity of network outcomes," Journal of Business Research, Elsevier, vol. 129(C), pages 555-561.
    5. Rojko, Katarina & Lužar, Borut, 2022. "Scientific performance across research disciplines: Trends and differences in the case of Slovenia," Journal of Informetrics, Elsevier, vol. 16(2).
    6. Matteo Cinelli & Giovanna Ferraro & Antonio Iovanella, 2022. "Connections matter: a proxy measure for evaluating network membership with an application to the Seventh Research Framework Programme," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3959-3976, July.
    7. Bornmann, Lutz & Williams, Richard, 2017. "Can the journal impact factor be used as a criterion for the selection of junior researchers? A large-scale empirical study based on ResearcherID data," Journal of Informetrics, Elsevier, vol. 11(3), pages 788-799.
    8. Marek Kwiek, 2018. "High research productivity in vertically undifferentiated higher education systems: Who are the top performers?," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 415-462, April.

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