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Discrete and continuous conceptualizations of science: Implications for knowledge domain visualization

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  • Skupin, André

Abstract

Visual depiction of the structure and evolution of science has been proposed as a key strategy for dealing with the large, complex, and increasingly interdisciplinary records of scientific communication. While every such visualization assumes the existence of spatial structures within the system of science, new methods and tools are rarely linked to thorough reflection on the underlying spatial concepts. Meanwhile, geographic information science has adopted a view of geographic space as conceptualized through the duality of discrete objects and continuous fields. This paper argues that conceptualization of science has been dominated by a view of its constituent elements (e.g., authors, articles, journals, disciplines) as discrete objects. It is proposed that, like in geographic information science, alternative concepts could be used for the same phenomenon. For example, one could view an author as either a discrete object at a specific location or as a continuous field occupying all of a discipline. It is further proposed that this duality of spatial concepts can extend to the methods by which low-dimensional geometric models of high-dimensional scientific spaces are created and used. This can result in new methods revealing different kinds of insights. This is demonstrated by a juxtaposition of two visualizations of an author's intellectual evolution on the basis of either a discrete or continuous conceptualization.

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  • Skupin, André, 2009. "Discrete and continuous conceptualizations of science: Implications for knowledge domain visualization," Journal of Informetrics, Elsevier, vol. 3(3), pages 233-245.
    • Handle: RePEc:eee:infome:v:3:y:2009:i:3:p:233-245
    DOI: 10.1016/j.joi.2009.03.002
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    References listed on IDEAS

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    1. James A. Wise, 1999. "The ecological approach to text visualization," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(13), pages 1224-1233.
    2. Howard D. White, 2001. "Author-centered bibliometrics through CAMEOs: Characterizations automatically made and edited online," Scientometrics, Springer;Akadémiai Kiadó, vol. 51(3), pages 607-637, July.
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    Cited by:

    1. An, Lu & Yu, Chuanming & Li, Gang, 2014. "Visual topical analysis of Chinese and American Library and Information Science research institutions," Journal of Informetrics, Elsevier, vol. 8(1), pages 217-233.
    2. Frenken, Koen & Hardeman, Sjoerd & Hoekman, Jarno, 2009. "Spatial scientometrics: Towards a cumulative research program," Journal of Informetrics, Elsevier, vol. 3(3), pages 222-232.
    3. Irena Sajovic & Bojana Boh Podgornik, 2022. "Bibliometric Analysis of Visualizations in Computer Graphics: A Study," SAGE Open, , vol. 12(1), pages 21582440211, January.
    4. Debnath, Ramit & darby, Sarah & Bardhan, Ronita & Mohaddes, Kamiar & Sunikka-Blank, Minna, 2020. "A nested computational social science approach for deep-narrative analysis in energy policy research," SocArXiv hvcb5, Center for Open Science.
    5. Cobo, M.J. & López-Herrera, A.G. & Herrera-Viedma, E. & Herrera, F., 2011. "An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field," Journal of Informetrics, Elsevier, vol. 5(1), pages 146-166.
    6. Meyer, Eric T. & Schroeder, Ralph, 2009. "Untangling the web of e-Research: Towards a sociology of online knowledge," Journal of Informetrics, Elsevier, vol. 3(3), pages 246-260.

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