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The extraction of community structures from publication networks to support ethnographic observations of field differences in scientific communication

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  • Theresa Velden
  • Carl Lagoze

Abstract

The scientific community of researchers in a research specialty is an important unit of analysis for understanding the field‐specific shaping of scientific communication practices. These scientific communities are, however, a challenging unit of analysis to capture and compare because they overlap, have fuzzy boundaries, and evolve over time. We describe a network analytic approach that reveals the complexities of these communities through the examination of their publication networks in combination with insights from ethnographic field studies. We suggest that the structures revealed indicate overlapping subcommunities within a research specialty, and we provide evidence that they differ in disciplinary orientation and research practices. By mapping the community structures of scientific fields we increase confidence about the domain of validity of ethnographic observations as well as of collaborative patterns extracted from publication networks thereby enabling the systematic study of field differences. The network analytic methods presented include methods to optimize the delineation of a bibliographic data set to adequately represent a research specialty and methods to extract community structures from this data. We demonstrate the application of these methods in a case study of two research specialties in the physical and chemical sciences.

Suggested Citation

  • Theresa Velden & Carl Lagoze, 2013. "The extraction of community structures from publication networks to support ethnographic observations of field differences in scientific communication," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(12), pages 2405-2427, December.
  • Handle: RePEc:bla:jamist:v:64:y:2013:i:12:p:2405-2427
    DOI: 10.1002/asi.22929
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    Cited by:

    1. Yongli Li & Guijie Zhang & Yuqiang Feng & Chong Wu, 2015. "An entropy-based social network community detecting method and its application to scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 1003-1017, January.
    2. Liliana Arroyo Moliner & Eva Gallardo-Gallardo & Pedro Gallo de Puelles, 2017. "Understanding scientific communities: a social network approach to collaborations in Talent Management research," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1439-1462, December.
    3. Theresa Velden & Kevin W. Boyack & Jochen Gläser & Rob Koopman & Andrea Scharnhorst & Shenghui Wang, 2017. "Comparison of topic extraction approaches and their results," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1169-1221, May.
    4. Theresa Velden & Shiyan Yan & Carl Lagoze, 2017. "Mapping the cognitive structure of astrophysics by infomap clustering of the citation network and topic affinity analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1033-1051, May.

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