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Conceptualizing the interdisciplinary diffusion and evolution of emerging fields: The case of systems biology

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  • Goldman, Alyssa W.

Abstract

This paper contributes to the longitudinal study and representation of the diffusion of scholarly knowledge through bibliometrics. The case of systems biology is used to illustrate a means for considering the structure and different roles of journals in the diffusion of a relatively new field to diverse subject areas. Using a bipartite network analysis of journals and subject categories, a core–intermediary–periphery diffusion structure is detected through comparative analysis of betweenness centrality over time. Systems biology diffuses from a core of foundational, theoretical areas to more specific, applied, practical fields, most of which relate to human health. Next, cluster analysis is applied to subject category co-occurrence networks to longitudinally trace the movement of fields within the core–intermediary–periphery structure. The results of these analyses reveal patterns of systems biology's diffusion across both theoretical and applied fields, and are also used to suggest how the dynamics of a field's interdisciplinary evolution can be realized. The author concludes by presenting a typology for considering how journals may function to support attributes of the core–intermediary–periphery structure and diffusion patterns more broadly.

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  • Goldman, Alyssa W., 2014. "Conceptualizing the interdisciplinary diffusion and evolution of emerging fields: The case of systems biology," Journal of Informetrics, Elsevier, vol. 8(1), pages 43-58.
    • Handle: RePEc:eee:infome:v:8:y:2014:i:1:p:43-58
    DOI: 10.1016/j.joi.2013.10.009
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    Cited by:

    1. Gregorio González-Alcaide & Pedro Llorente & José M. Ramos, 2016. "Bibliometric indicators to identify emerging research fields: publications on mass gatherings," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1283-1298, November.
    2. Jeong, Yoo Kyung & Xie, Qing & Yan, Erjia & Song, Min, 2020. "Examining drug and side effect relation using author–entity pair bipartite networks," Journal of Informetrics, Elsevier, vol. 14(1).
    3. June Young Lee & Sejung Ahn & Dohyun Kim, 2021. "Deep learning-based prediction of future growth potential of technologies," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-16, June.
    4. Yawen Zou & Manfred D. Laubichler, 2017. "Measuring the contributions of Chinese scholars to the research field of systems biology from 2005 to 2013," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1615-1631, March.

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