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Classification of individual articles from all of science by research level

Author

Listed:
  • Boyack, Kevin W.
  • Patek, Michael
  • Ungar, Lyle H.
  • Yoon, Patrick
  • Klavans, Richard

Abstract

A system of four research levels, designed to classify scientific journals from most applied to most basic, was introduced by Francis Narin and colleagues in the 1970s. Research levels have been used since that time to characterize research at institutional and departmental levels. Currently, less than half of all articles published are in journals that been classified by research level. There is thus a need for the notion of research level to be extended in a way that all articles can be so classified. This article reports on a new model – trained from title and abstract words and cited references – that classifies individual articles by research level. The model covers all of science, and has been used to classify over 25 million articles from Scopus by research level. The final model and set of classified articles are further characterized.

Suggested Citation

  • Boyack, Kevin W. & Patek, Michael & Ungar, Lyle H. & Yoon, Patrick & Klavans, Richard, 2014. "Classification of individual articles from all of science by research level," Journal of Informetrics, Elsevier, vol. 8(1), pages 1-12.
    • Handle: RePEc:eee:infome:v:8:y:2014:i:1:p:1-12
    DOI: 10.1016/j.joi.2013.10.005
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    References listed on IDEAS

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    1. McMillan, G. Steven & Narin, Francis & Deeds, David L., 2000. "An analysis of the critical role of public science in innovation: the case of biotechnology," Research Policy, Elsevier, vol. 29(1), pages 1-8, January.
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    3. Francis Narin & Gabriel Pinski & Helen Hofer Gee, 1976. "Structure of the Biomedical Literature," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 27(1), pages 25-45, January.
    4. Grant Lewison & Guillermo Paraje, 2004. "The classification of biomedical journals by research level," Scientometrics, Springer;Akadémiai Kiadó, vol. 60(2), pages 145-157, June.
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    Citations

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    Cited by:

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    3. Hyeonchae Yang & Woo-Sung Jung, 2015. "A strategic management approach for Korean public research institutes based on bibliometric investigation," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(4), pages 1437-1464, July.
    4. Lin Zhang & Gunnar Sivertsen & Huiying Du & Ying Huang & Wolfgang Glänzel, 2021. "Gender differences in the aims and impacts of research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8861-8886, November.
    5. Anckaert, Paul-Emmanuel & Cassiman, David & Cassiman, Bruno, 2020. "Fostering practice-oriented and use-inspired science in biomedical research," Research Policy, Elsevier, vol. 49(2).
    6. Xin Li & Xuli Tang & Wei Lu, 2023. "Tracking biomedical articles along the translational continuum: a measure based on biomedical knowledge representation," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1295-1319, February.
    7. Bastian Rake & Pablo D’Este & Maureen McKelvey, 2021. "Exploring network dynamics in science: the formation of ties to knowledge translators in clinical research," Journal of Evolutionary Economics, Springer, vol. 31(5), pages 1433-1464, November.
    8. Loet Leydesdorff & Caroline S. Wagner & Lutz Bornmann, 2018. "Betweenness and diversity in journal citation networks as measures of interdisciplinarity—A tribute to Eugene Garfield," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 567-592, February.
    9. Zhang, Lin & Sivertsen, Gunnar & Du, Huiying & HUANG, Ying & Glänzel, Wolfgang, 2021. "Gender differences in the aims and impacts of research," SocArXiv 9n347, Center for Open Science.
    10. Li, Xin & Tang, Xuli & Cheng, Qikai, 2022. "Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network," Journal of Informetrics, Elsevier, vol. 16(4).
    11. Dongyu Zang & Chunli Liu, 2023. "Exploring the clinical translation intensity of papers published by the world’s top scientists in basic medicine," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2371-2416, April.
    12. Tan Tran, 2020. "R&D and Knowledge Expertise of French Regions," Papers in Evolutionary Economic Geography (PEEG) 2004, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Feb 2020.
    13. Kenneth Zahringer & Christos Kolympiris & Nicholas Kalaitzandonakes, 2017. "Academic knowledge quality differentials and the quality of firm innovation," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(5), pages 821-844.
    14. Paul Donner & Ulrich Schmoch, 2020. "The implicit preference of bibliometrics for basic research," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1411-1419, August.
    15. Robert J. W. Tijssen & Jos Winnink, 2016. "Twenty-first century macro-trends in the institutional fabric of science: bibliometric monitoring and analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 2181-2194, December.
    16. Li, Xin & Tang, Xuli, 2021. "Characterizing interdisciplinarity in drug research: A translational science perspective," Journal of Informetrics, Elsevier, vol. 15(4).
    17. Sitaram Devarakonda & Dmitriy Korobskiy & Tandy Warnow & George Chacko, 2020. "Viewing computer science through citation analysis: Salton and Bergmark Redux," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 271-287, October.
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    19. Du, Jian & Li, Peixin & Guo, Qianying & Tang, Xiaoli, 2019. "Measuring the knowledge translation and convergence in pharmaceutical innovation by funding-science-technology-innovation linkages analysis," Journal of Informetrics, Elsevier, vol. 13(1), pages 132-148.

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