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Maps of science as interdisciplinary discourse: co-citation contexts and the role of analogy

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  • Henry Small

    (Thomson Reuters)

Abstract

Interdisciplinarity can be manifest in many forms: through collaboration or communication between scientists working in different fields or through the work of individual scientists who employ concepts or methods across disciplines. This latter form of interdisciplinarity is addressed here with the goal of understanding how ideas in different fields come together to create new opportunities for discovery. Maps of science are used to suggest possible interdisciplinary links which are then analyzed by co-citation context analysis. Interdisciplinary links are identified by juxtaposing a clustering and mapping of documents against a journal-based categorization of the same document clusters. Links between clusters are characterized as interdisciplinary based on the dissonance of their category assignments. To verify and probe more deeply into the meaning of interdisciplinary links, co-citation contexts for selected links from five separate cases are analyzed in terms of prominent cue words. This analysis reveals that interdisciplinary connections are often based on authors’ perceptions of analogous problems across scientific domains. Cue words drawn from the citation contexts also suggest that these connections are viewed as important and ripe with both opportunity and risk.

Suggested Citation

  • Henry Small, 2010. "Maps of science as interdisciplinary discourse: co-citation contexts and the role of analogy," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 835-849, June.
    • Handle: RePEc:spr:scient:v:83:y:2010:i:3:d:10.1007_s11192-009-0121-z
    DOI: 10.1007/s11192-009-0121-z
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    References listed on IDEAS

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    Cited by:

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    2. Jiming Hu & Yin Zhang, 2017. "Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 91-109, July.
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    4. Alexey Lyutov & Yilmaz Uygun & Marc-Thorsten Hütt, 2021. "Machine learning misclassification of academic publications reveals non-trivial interdependencies of scientific disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1173-1186, February.
    5. Richard Heidler & Olof Hallonsten, 2015. "Qualifying the performance evaluation of Big Science beyond productivity, impact and costs," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 295-312, July.
    6. Henry Small, 2011. "Interpreting maps of science using citation context sentiments: a preliminary investigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(2), pages 373-388, May.
    7. Aparna Basu & Roland Wagner Dobler, 2012. "‘Cognitive mobility’ or migration of authors between fields used in mapping a network of mathematics," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 353-368, May.
    8. Mingyang Wang & Jiaqi Zhang & Guangsheng Chen & Kah-Hin Chai, 2019. "Examining the influence of open access on journals’ citation obsolescence by modeling the actual citation process," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1621-1641, June.
    9. David Gunnarsson Lorentzen, 2014. "Webometrics benefitting from web mining? An investigation of methods and applications of two research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 409-445, May.
    10. Jimi Adams & Ryan Light, 2014. "Mapping Interdisciplinary Fields: Efficiencies, Gaps and Redundancies in HIV/AIDS Research," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-13, December.
    11. Chen, Shiji & Qiu, Junping & Arsenault, Clément & Larivière, Vincent, 2021. "Exploring the interdisciplinarity patterns of highly cited papers," Journal of Informetrics, Elsevier, vol. 15(1).
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