Interdisciplinarity as Diversity in Citation Patterns among Journals: Rao-Stirling Diversity, Relative Variety, and the Gini coefficient
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Abstract (arXiv)
Questions of definition and measurement continue to constrain a consensus on the measurement of interdisciplinarity. Using Rao-Stirling (RS) Diversity produces sometimes anomalous results. We argue that these unexpected outcomes can be related to the use of "dual-concept diversity" which combines "variety" and "balance" in the definitions (ex ante). We propose to modify RS Diversity into a new indicator (DIV) which operationalizes variety, balance, and disparity independently and then combines them ex post. "Balance" can be measured using the Gini coefficient. We apply DIV to the aggregated citation patterns of 11,487 journals covered by the Journal Citation Reports 2016 of the Science Citation Index and the Social Sciences Citation Index as an empirical domain and, in more detail, to the citation patterns of 85 journals assigned to the Web-of-Science category "information science & library science" in both the cited and citing directions. We compare the results of the indicators and show that DIV provides improved results in terms of distinguishing between interdisciplinary knowledge integration (citing) versus knowledge diffusion (cited). The new diversity indicator and RS diversity measure different features. A routine for the measurement of the various operationalizations of diversity (in any data matrix) is made available online.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jul 2018.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 4.
7 papers.
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- 2024Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer Sciencecited by 4
- 2025A survey of diversity quantification in natural language processing: The why, what, where and howcited by 4
- 2025Research Borderlands: Analysing Writing Across Research Culturescited by 3
- 2025The Cost-Benefit of Interdisciplinarity in AI for Mental Healthcited by 0
- 2025Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Biascited by 0
- 2025Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias5 Dec 2025
- 2025The Cost-Benefit of Interdisciplinarity in AI for Mental Health21 Oct 2025
- 2025A survey of diversity quantification in natural language processing: The why, what, where and how28 Jul 2025
- 2025Research Borderlands: Analysing Writing Across Research Cultures1 Jun 2025
- 2024Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer Science29 Sep 2024
- 2023We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields23 Oct 2023
- 2022Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications19 Dec 2022