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Manifestation of emerging specialties in journal literature: A growth model of papers, references, exemplars, bibliographic coupling, cocitation, and clustering coefficient distribution

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  • Steven A. Morris

Abstract

A model is presented of the manifestation of the birth and development of a scientific specialty in a collection of journal papers. The proposed model, Cumulative Advantage by Paper with Exemplars (CAPE) is an adaptation of Price's cumulative advantage model (D. Price, 1976). Two modifications are made: (a) references are cited in groups by paper, and (b) the model accounts for the generation of highly cited exemplar references immediately after the birth of the specialty. This simple growth process mimics many characteristic features of real collections of papers, including the structure of the paper‐to‐reference matrix, the reference‐per‐paper distribution, the paper‐per‐reference distribution, the bibliographic coupling distribution, the cocitation distribution, the bibliographic coupling clustering coefficient distribution, and the temporal distribution of exemplar references. The model yields a great deal of insight into the process that produces the connectedness and clustering of a collection of articles and references. Two examples are presented and successfully modeled: a collection of 131 articles on MEMS RF (microelectromechnical systems radio frequency) switches, and a collection of 901 articles on the subject of complex networks.

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  • Steven A. Morris, 2005. "Manifestation of emerging specialties in journal literature: A growth model of papers, references, exemplars, bibliographic coupling, cocitation, and clustering coefficient distribution," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 56(12), pages 1250-1273, October.
  • Handle: RePEc:bla:jamist:v:56:y:2005:i:12:p:1250-1273
    DOI: 10.1002/asi.20208
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    Cited by:

    1. Chatterjee Kalyan & Chowdhury Avantika, 2012. "Formation of Citation Networks by Rational Players and The Diffusion of Ideas," Review of Network Economics, De Gruyter, vol. 11(3), pages 1-38, September.
    2. Kim, Hyoungshick & Yoon, Ji Won & Crowcroft, Jon, 2012. "Network analysis of temporal trends in scholarly research productivity," Journal of Informetrics, Elsevier, vol. 6(1), pages 97-110.
    3. Vîiu, Gabriel-Alexandru, 2018. "The lognormal distribution explains the remarkable pattern documented by characteristic scores and scales in scientometrics," Journal of Informetrics, Elsevier, vol. 12(2), pages 401-415.
    4. Ausloos, M., 2015. "Assessing the true role of coauthors in the h-index measure of an author scientific impact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 422(C), pages 136-142.
    5. Werner Marx & Lutz Bornmann, 2010. "How accurately does Thomas Kuhn’s model of paradigm change describe the transition from the static view of the universe to the big bang theory in cosmology?," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 441-464, August.
    6. Theresa Velden & Asif-ul Haque & Carl Lagoze, 2010. "A new approach to analyzing patterns of collaboration in co-authorship networks: mesoscopic analysis and interpretation," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 219-242, October.
    7. Reindert K. Buter & Ed. C. M. Noyons & Anthony F. J. Raan, 2011. "Searching for converging research using field to field citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(2), pages 325-338, February.

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