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Relative age of references as a tool to identify emerging research fields with an application to the field of ecology and environmental sciences

Author

Listed:
  • Ivan Jarić

    (University of Belgrade)

  • Jelena Knežević-Jarić

    (University of Belgrade)

  • Mirjana Lenhardt

    (University of Belgrade)

Abstract

Emerging scientific fields are commonly identified by different citation based bibliometric parameters. However, their main shortcoming is the existence of a time lag needed for a publication to receive citations. In the present study, we assessed the relationship between the age of references in scientific publications and the change in publication rate within a research field. Two indices based on the age of references are presented, the relative age of references and the ratio of references published during the preceding 2 years, and applied thereafter on four datasets from the previously published studies, which assessed eutrophication research, sturgeon research, fisheries research, and the general field of ecology. We observed a consistent pattern that the emerging research topics had a lower median age of references and a higher ratio of references published in the preceding 2 years than their respective general research fields. The main advantage of indices based on the age of references is that they are not influenced by a time lag, and as such they are able to provide insight into current scientific trends. The best potential of the presented indices is to use them combined with other approaches, as each one can reveal different aspects and properties of the assessed data, and provide validation of the obtained results. Their use should be however assessed further before they are employed as standard tools by scientists, science managers and policy makers.

Suggested Citation

  • Ivan Jarić & Jelena Knežević-Jarić & Mirjana Lenhardt, 2014. "Relative age of references as a tool to identify emerging research fields with an application to the field of ecology and environmental sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 519-529, August.
    • Handle: RePEc:spr:scient:v:100:y:2014:i:2:d:10.1007_s11192-014-1268-9
    DOI: 10.1007/s11192-014-1268-9
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    References listed on IDEAS

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    7. Xu, Shuo & Hao, Liyuan & An, Xin & Yang, Guancan & Wang, Feifei, 2019. "Emerging research topics detection with multiple machine learning models," Journal of Informetrics, Elsevier, vol. 13(4).
    8. Serhat Burmaoglu & Olivier Sartenaer & Alan Porter & Munan Li, 2019. "Analysing the theoretical roots of technology emergence: an evolutionary perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 97-118, April.

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