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The dynamics of exchanges and references among scientific texts, and the autopoiesis of discursive knowledge

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  • Lucio-Arias, Diana
  • Leydesdorff, Loet

Abstract

Discursive knowledge emerges as codification in flows of communication. The flows of communication are constrained and enabled by networks of communications as their historical manifestations at each moment of time. New publications modify the existing networks by changing the distributions of attributes and relations in document sets, while the networks are self-referentially updated along trajectories. Codification operates reflexively: the network structures are reconstructed from the perspective of hindsight. Codification along different axes differentiates discursive knowledge into specialties. These intellectual control structures are constructed bottom-up, but feed top-down back upon the production of new knowledge. However, the forward dynamics of diffusion in the development of the communication networks along trajectories differs from the feedback mechanisms of control. Analysis of the development of scientific communication in terms of evolving scientific literatures provides us with a model which makes these evolutionary processes amenable to measurement.

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  • Lucio-Arias, Diana & Leydesdorff, Loet, 2009. "The dynamics of exchanges and references among scientific texts, and the autopoiesis of discursive knowledge," Journal of Informetrics, Elsevier, vol. 3(3), pages 261-271.
    • Handle: RePEc:eee:infome:v:3:y:2009:i:3:p:261-271
    DOI: 10.1016/j.joi.2009.03.003
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    Cited by:

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    3. Park, Han Woo & Leydesdorff, Loet, 2013. "Decomposing social and semantic networks in emerging “big data” research," Journal of Informetrics, Elsevier, vol. 7(3), pages 756-765.
    4. Ryan Light & jimi adams, 2016. "Knowledge in motion: the evolution of HIV/AIDS research," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1227-1248, June.
    5. Gaston Heimeriks & Ron Boschma, 2014. "The path- and place-dependent nature of scientific knowledge production in biotech 1986–2008," Journal of Economic Geography, Oxford University Press, vol. 14(2), pages 339-364.
    6. Gaston Heimeriks, 2009. "Governing science as a complex adaptive system," Innovation Studies Utrecht (ISU) working paper series 09-16, Utrecht University, Department of Innovation Studies, revised Nov 2009.
    7. Frenken, Koen & Hardeman, Sjoerd & Hoekman, Jarno, 2009. "Spatial scientometrics: Towards a cumulative research program," Journal of Informetrics, Elsevier, vol. 3(3), pages 222-232.
    8. Burmaoglu, Serhat & Sartenaer, Olivier & Porter, Alan, 2019. "Conceptual definition of technology emergence: A long journey from philosophy of science to science policy," Technology in Society, Elsevier, vol. 59(C).
    9. 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.
    10. 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.
    11. Frank Bakker & Iina Hellsten, 2013. "Capturing Online Presence: Hyperlinks and Semantic Networks in Activist Group Websites on Corporate Social Responsibility," Journal of Business Ethics, Springer, vol. 118(4), pages 807-823, December.
    12. Staša Milojević & Loet Leydesdorff, 2013. "Information metrics (iMetrics): a research specialty with a socio-cognitive identity?," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 141-157, April.
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