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Diversity and interdisciplinarity: Should variety, balance and disparity be combined as a product or better as a sum? An information-theoretical and statistical estimation approach

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  • Rüdiger Mutz

    (University of Zurich)

Abstract

Diversity is a central concept not only in ecology, but also in the social sciences and in bibliometrics. The discussion about an adequate measure of diversity is strongly driven by the work of Rao (Sankhyā Indian J Stat Series A 44:1-22, 1982) and Stirling (J R Soc Interface 4:707-719, 2007). It is to the credit of Leydesdorff (Scientometr 116:2113-2121, 2018) to have proposed a decisive improvement with regard to an inconsistency in the Rao-Sterling-diversity indicator that Rousseau (Scientometr 116:645-653, 2018) had pointed out. With recourse to Shannon's probabilistically based entropy concept, in this contribution the three components of diversity “variety”, “balance”, and “disparity” are to be reconceptualized as entropy masses that add up to an overall diversity indicator dive. Diversity can thus be interpreted as the degree of uncertainty or unpredictability. For "disparity", for example, the concept of mutual information is used. However, probabilities must be estimated statistically. A basic estimation strategy (cross tables) and a more sophisticated one (parametric statistical model) are presented. This overall probability-theoretical based concept is applied exemplarily to data on research output types of funded research projects in UK that were the subject of the Metric Tide Report (REF 2014) and ex-ante evaluation data of a research funding organization. As expected, research output types depend on the research area, with journal articles having the strongest individual balance among the output types, i.e., being represented in almost all research areas. For the ex-ante evaluation data of 1,221 funded projects the diversity components were statistically estimated. The overall diversity of the projects in terms of entropy is 55.5% of the maximal possible entropy.

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  • Rüdiger Mutz, 2022. "Diversity and interdisciplinarity: Should variety, balance and disparity be combined as a product or better as a sum? An information-theoretical and statistical estimation approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7397-7414, December.
  • Handle: RePEc:spr:scient:v:127:y:2022:i:12:d:10.1007_s11192-022-04336-3
    DOI: 10.1007/s11192-022-04336-3
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    References listed on IDEAS

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    1. Lutz Bornmann & Rüdiger Mutz & Robin Haunschild & Felix Moya-Anegon & Mirko Almeida Madeira Clemente & Moritz Stefaner, 2021. "Mapping the impact of papers on various status groups in excellencemapping.net: a new release of the excellence mapping tool based on citation and reader scores," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9305-9331, November.
    2. Manuel Goyanes & Márton Demeter & Aurea Grané & Irene Albarrán-Lozano & Homero Gil de Zúñiga, 2020. "A mathematical approach to assess research diversity: operationalization and applicability in communication sciences, political science, and beyond," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2299-2322, December.
    3. Rüdiger Mutz & Lutz Bornmann & Hans-Dieter Daniel, 2012. "Types of research output profiles: A multilevel latent class analysis of the Austrian Science Fund's final project report data," Research Evaluation, Oxford University Press, vol. 22(2), pages 118-133, December.
    4. Loet Leydesdorff, 2018. "Diversity and interdisciplinarity: how can one distinguish and recombine disparity, variety, and balance?," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 2113-2121, September.
    5. Andy Stirling, 2007. "A General Framework for Analysing Diversity in Science, Technology and Society," SPRU Working Paper Series 156, SPRU - Science Policy Research Unit, University of Sussex Business School.
    6. Per Ahlgren & Bo Jarneving & Ronald Rousseau, 2003. "Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(6), pages 550-560, April.
    7. Jian Wang & Bart Thijs & Wolfgang Glänzel, 2015. "Interdisciplinarity and Impact: Distinct Effects of Variety, Balance, and Disparity," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
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    9. Leydesdorff, Loet & Wagner, Caroline S. & Bornmann, Lutz, 2019. "Interdisciplinarity as diversity in citation patterns among journals: Rao-Stirling diversity, relative variety, and the Gini coefficient," Journal of Informetrics, Elsevier, vol. 13(1), pages 255-269.
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