IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v8y2014i1p1-12.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157713000825
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2013.10.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Narin, Francis & Rozek, Richard P., 1988. "Bibliometric analysis of U.S. pharmaceutical industry research performance," Research Policy, Elsevier, vol. 17(3), pages 139-154, June.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lawson, Cornelia & Salter, Ammon, 2023. "Exploring the effect of overlapping institutional applications on panel decision-making," Research Policy, Elsevier, vol. 52(9).
    2. Belén Álvarez-Bornstein & Adrián A. Díaz-Faes & María Bordons, 2019. "What characterises funded biomedical research? Evidence from a basic and a clinical domain," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 805-825, May.
    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.
    18. Rons, Nadine, 2018. "Bibliometric approximation of a scientific specialty by combining key sources, title words, authors and references," Journal of Informetrics, Elsevier, vol. 12(1), pages 113-132.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. Ke, Qing, 2020. "Technological impact of biomedical research: The role of basicness and novelty," Research Policy, Elsevier, vol. 49(7).
    6. Albert Banal-Estañol & Inés Macho-Stadler, 2007. "Financial Incentives in Academia: Research versus Development," Working Papers 295, Barcelona School of Economics.
    7. G. Steven McMillan & Robert D. Hamilton, 2007. "The public science base of US biotechnology: A citation-weighted approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(1), pages 3-10, July.
    8. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
    9. Anckaert, Paul-Emmanuel & Cassiman, David & Cassiman, Bruno, 2020. "Fostering practice-oriented and use-inspired science in biomedical research," Research Policy, Elsevier, vol. 49(2).
    10. René Belderbos & Nazareno Braito & Jian Wang, 2024. "Heterogeneous university research and firm R&D location decisions: research orientation, academic quality, and investment type," The Journal of Technology Transfer, Springer, vol. 49(5), pages 1959-1989, October.
    11. Larsen, Maria Theresa, 2011. "The implications of academic enterprise for public science: An overview of the empirical evidence," Research Policy, Elsevier, vol. 40(1), pages 6-19, February.
    12. Rons, Nadine, 2018. "Bibliometric approximation of a scientific specialty by combining key sources, title words, authors and references," Journal of Informetrics, Elsevier, vol. 12(1), pages 113-132.
    13. Ke, Qing, 2020. "An analysis of the evolution of science-technology linkage in biomedicine," Journal of Informetrics, Elsevier, vol. 14(4).
    14. 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.
    15. Ke, Qing, 2018. "Comparing scientific and technological impact of biomedical research," Journal of Informetrics, Elsevier, vol. 12(3), pages 706-717.
    16. G. Steven McMillan, 2009. "Gender differences in patenting activity: An examination of the US biotechnology industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(3), pages 683-691, September.
    17. Xin Li & Xuli Tang & Wei Lu, 2024. "How biomedical papers accumulated their clinical citations: a large-scale retrospective analysis based on PubMed," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3315-3339, June.
    18. Sternitzke, Christian, 2010. "Knowledge sources, patent protection, and commercialization of pharmaceutical innovations," Research Policy, Elsevier, vol. 39(6), pages 810-821, July.
    19. Li, Xin & Tang, Xuli, 2021. "Characterizing interdisciplinarity in drug research: A translational science perspective," Journal of Informetrics, Elsevier, vol. 15(4).
    20. Ke, Qing, 2020. "The citation disadvantage of clinical research," Journal of Informetrics, Elsevier, vol. 14(1).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:infome:v:8:y:2014:i:1:p:1-12. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.