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On entropy research analysis: cross-disciplinary knowledge transfer

Author

Listed:
  • R. Basurto-Flores

    (Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas)

  • L. Guzmán-Vargas

    (Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas)

  • S. Velasco

    (Universidad de Salamanca
    Universidad de Salamanca)

  • A. Medina

    (Universidad de Salamanca)

  • A. Calvo Hernandez

    (Universidad de Salamanca
    Universidad de Salamanca)

Abstract

Our aim is to illustrate how the thermodynamics-based concept of entropy has spread across different areas of knowledge by analyzing the distribution of papers, citations and the use of words related to entropy in the predefined Scopus categories. To achieve this, we analyze the Scopus papers database related to entropy research during the last 20 years, collecting 750 K research papers which directly contain or mention the word entropy. First, some well-recognized works which introduced novel entropy-related definitions are monitored. Then we compare the hierarchical structure which emerges for the different cases of association, which can be in terms of citations among papers, classification of papers in categories or key words in abstracts and titles. Our study allowed us to evaluate, to some extent, the utility and versatility of concepts such as entropy to permeate in different areas of science. Furthermore, the use of specific terms (key words) in titles and abstracts provided a useful way to account for the interaction between areas in the category research space.

Suggested Citation

  • R. Basurto-Flores & L. Guzmán-Vargas & S. Velasco & A. Medina & A. Calvo Hernandez, 2018. "On entropy research analysis: cross-disciplinary knowledge transfer," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 123-139, October.
    • Handle: RePEc:spr:scient:v:117:y:2018:i:1:d:10.1007_s11192-018-2860-1
    DOI: 10.1007/s11192-018-2860-1
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    1. Michal Brzezinski, 2015. "Power laws in citation distributions: evidence from Scopus," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 213-228, April.
    2. O. Mryglod & Yu. Holovatch & R. Kenna & B. Berche, 2016. "Quantifying the evolution of a scientific topic: reaction of the academic community to the Chornobyl disaster," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1151-1166, March.
    3. Filippo Radicchi & Claudio Castellano, 2012. "A Reverse Engineering Approach to the Suppression of Citation Biases Reveals Universal Properties of Citation Distributions," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-9, March.
    4. Leo Egghe & Ronald Rousseau, 2000. "Partial orders and measures for language preferences," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 51(12), pages 1123-1130.
    5. Ed J. Rinia & Thed N. van Leeuwen & Eppo E. W. Bruins & Hendrik G. van Vuren & Anthony F. J. van Raan, 2002. "Measuring knowledge transfer between fields of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 54(3), pages 347-362, July.
    6. Johan Bollen & Herbert Van de Sompel & Aric Hagberg & Luis Bettencourt & Ryan Chute & Marko A Rodriguez & Lyudmila Balakireva, 2009. "Clickstream Data Yields High-Resolution Maps of Science," PLOS ONE, Public Library of Science, vol. 4(3), pages 1-11, March.
    7. Gangan Prathap, 2011. "The Energy–Exergy–Entropy (or EEE) sequences in bibliometric assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 515-524, June.
    8. Kevin W. Boyack & Richard Klavans & Katy Börner, 2005. "Mapping the backbone of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(3), pages 351-374, August.
    9. Pedro Albarrán & Javier Ruiz‐Castillo, 2011. "References made and citations received by scientific articles," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(1), pages 40-49, January.
    10. Nathaniel D Osgood & Tuhin Paul & Kevin G Stanley & Weicheng Qian, 2016. "A Theoretical Basis for Entropy-Scaling Effects in Human Mobility Patterns," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-21, August.
    11. Loet Leydesdorff & Ismael Rafols, 2009. "A global map of science based on the ISI subject categories," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(2), pages 348-362, February.
    12. Bekiros, Stelios & Nguyen, Duc Khuong & Sandoval Junior, Leonidas & Uddin, Gazi Salah, 2017. "Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets," European Journal of Operational Research, Elsevier, vol. 256(3), pages 945-961.
    13. Arnab Chatterjee & Asim Ghosh & Bikas K Chakrabarti, 2016. "Universality of Citation Distributions for Academic Institutions and Journals," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-11, January.
    14. Miguel R. Guevara & Dominik Hartmann & Manuel Aristarán & Marcelo Mendoza & César A. Hidalgo, 2016. "The research space: using career paths to predict the evolution of the research output of individuals, institutions, and nations," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1695-1709, December.
    15. Almeida, M.P., 2001. "Generalized entropies from first principles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 300(3), pages 424-432.
    16. Gangan Prathap, 2011. "Quasity, when quantity has a quality all of its own—toward a theory of performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 555-562, August.
    17. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
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    2. Andrade Valbuena, Nelson A. & Valenzuela Fernández, Leslier & Merigó, José M., 2022. "Thirty-five years of strategic management research. A country analysis using bibliometric techniques for the 1987-2021 period," Cuadernos de Gestión, Universidad del País Vasco - Instituto de Economía Aplicada a la Empresa (IEAE).

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