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Towards prediction of paradigm shifts from scientific literature

Author

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  • Thara Prabhakaran

    (University of Kerala)

  • Hiran H. Lathabai

    (University of Kerala)

  • Susan George

    (University of Kerala)

  • Manoj Changat

    (University of Kerala)

Abstract

Amid the enormous volume of knowledge generated due to knowledge explosion, to a great extent, scientific literature mining can play a crucial role in research evaluation and tracking of important developments. Linked through citation relations, scientific literature forms network of papers or citation networks. Citation network analysis is gaining recognition as effective tool for research evaluation. Paradigm shift detection is of great importance to a multitude of beneficiaries and prediction of such paradigm shifts is of much greater gravity towards policy making. Recently, a metric named Flow Vergence (FV) gradient is proposed to detect paradigm shift pivots in scientific literature using the property named as FV effect. In this paper, its predictive power is investigated and tested statically and dynamically using publications in the field Nanotechnology for Engineering for a period 1989–2014. Validation included a post analysis validation of the field in 2017. As predictive power of FV gradient is confirmed, it can be regarded as an effective method for prediction of likely paradigm shifts and added to the tool kit of research evaluators and policy makers.

Suggested Citation

  • Thara Prabhakaran & Hiran H. Lathabai & Susan George & Manoj Changat, 2018. "Towards prediction of paradigm shifts from scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1611-1644, December.
    • Handle: RePEc:spr:scient:v:117:y:2018:i:3:d:10.1007_s11192-018-2931-3
    DOI: 10.1007/s11192-018-2931-3
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    References listed on IDEAS

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    6. Lathabai, Hiran H. & Prabhakaran, Thara & Changat, Manoj, 2015. "Centrality and Flow Vergence gradient based Path analysis of scientific literature: A case study of Biotechnology for Engineering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 157-168.
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    Cited by:

    1. Matteo Lascialfari & Marie-Benoît Magrini & Guillaume Cabanac, 2022. "Unpacking research lock-in through a diachronic analysis of topic cluster trajectories in scholarly publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6165-6189, November.
    2. Thara Prabhakaran & Hiran H. Lathabai & Susan George, 2019. "Competing, complementary and co-existing paradigms in techno-scientific literature: A case study of Nanotechnology for engineering," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 941-977, March.
    3. Xian Li & Ronald Rousseau & Liming Liang & Fangjie Xi & Yushuang Lü & Yifan Yuan & Xiaojun Hu, 2022. "Is low interdisciplinarity of references an unexpected characteristic of Nobel Prize winning research?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2105-2122, April.
    4. MaruÅ¡a Premru & Matej ÄŒerne & SaÅ¡a BatistiÄ, 2022. "The Road to the Future: A Multi-Technique Bibliometric Review and Development Projections of the Leader–Member Exchange (LMX) Research," SAGE Open, , vol. 12(2), pages 21582440221, May.

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