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Analysis of the field of physical chemistry of surfactants with the Unified Scienctometric Model. Fit of relational and activity indicators

Author

Listed:
  • R. Bailón-Moreno

    (Departamento de Ingeniería Química. Facultad de Ciencias, Campus de Fuentenueva Universidad de Granada)

  • E. Jurado-Alameda

    (Departamento de Ingeniería Química. Facultad de Ciencias, Campus de Fuentenueva Universidad de Granada)

  • R. Ruiz-Baños

    (Departamento de Biblioteconomía y Documentación, Facultad de Biblioteconomía y Documentación, Universidad de Granada)

  • J. P. Courtial

    (Laboratoire de Psychologie - Education - Cognition Développement (LabECD), Université de Nantes)

Abstract

Summary By the information system of CoPalRed© and with the treatment of 63,543 bibliographical references of scientific articles, the field of surfactants has been analysed in the light of the Unified Scientometric Model. It was found that the distributions of actors (countries, centres, and research laboratories, journals, researchers, key words of documents) fit Zif's Unified Law better than the Zipf-Mandelbrot Law. The model showed an especially good fit for relational indicators such as density and centrality. Using the Unified Bradford Law, the three zones fit were: core, straight fraction, and Groos droop. The fractality index was used to verify that Science can present fractal as well as transfractal structures. In conclusion, the Unified Scientometric Model is, for its flexibility and its integrating capacity, an appropriate model for representing Science, joining non-relational with relational Scientometrics under the same paradigm.

Suggested Citation

  • R. Bailón-Moreno & E. Jurado-Alameda & R. Ruiz-Baños & J. P. Courtial, 2005. "Analysis of the field of physical chemistry of surfactants with the Unified Scienctometric Model. Fit of relational and activity indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 63(2), pages 259-276, April.
    • Handle: RePEc:spr:scient:v:63:y:2005:i:2:d:10.1007_s11192-005-0212-4
    DOI: 10.1007/s11192-005-0212-4
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    Cited by:

    1. Judith Nyulas & Ștefan Dezsi & Adrian Niță & Raluca-Andreea Toma & Ana-Maria Lazăr, 2024. "Trends and Future Directions in Analysing Attractiveness of Geoparks Using an Automated Merging Method of Multiple Databases—R-Based Bibliometric Analysis," Land, MDPI, vol. 13(10), pages 1-30, October.
    2. Gaofeng Wang & Shuai Li & Zihao Zhang & Yanning Hou & Changhoon Shin, 2023. "A Visual Knowledge Map Analysis of Cross-Border Agri-Food Supply Chain Research Based on CiteSpace," Sustainability, MDPI, vol. 15(14), pages 1-28, July.
    3. Feng Hu & Wei Liu & Sang-Bing Tsai & Junbin Gao & Ning Bin & Quan Chen, 2018. "An Empirical Study on Visualizing the Intellectual Structure and Hotspots of Big Data Research from a Sustainable Perspective," Sustainability, MDPI, vol. 10(3), pages 1-19, March.
    4. Tianlong Yu & Hao Yang & Xiaowei Luo & Yifeng Jiang & Xiang Wu & Jingqi Gao, 2021. "Scientometric Analysis of Disaster Risk Perception: 2000–2020," IJERPH, MDPI, vol. 18(24), pages 1-19, December.
    5. Cobo, M.J. & López-Herrera, A.G. & Herrera-Viedma, E. & Herrera, F., 2011. "An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field," Journal of Informetrics, Elsevier, vol. 5(1), pages 146-166.
    6. Robert Tomaszewski, 2017. "Citations to chemical resources in scholarly articles: CRC Handbook of Chemistry and Physics and The Merck Index," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1865-1879, September.

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