IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v100y2014i3d10.1007_s11192-014-1319-2.html
   My bibliography  Save this article

A topic model approach to measuring interdisciplinarity at the National Science Foundation

Author

Listed:
  • Leah G. Nichols

    (National Science Foundation)

Abstract

As the National Science Foundation (NSF) implements new cross-cutting initiatives and programs, interest in assessing the success of these experiments in fostering interdisciplinarity grows. A primary challenge in measuring interdisciplinarity is identifying and bounding the discrete disciplines that comprise interdisciplinary work. Using statistical text-mining techniques to extract topic bins, the NSF recently developed a topic map of all of their awards issued between 2000 and 2011. These new data provide a novel means for measuring interdisciplinarity by assessing the language or content of award proposals. Using the Directorate for Social, Behavioral, and Economic Sciences as a case study and drawing on the new topic model of the NSF’s awards, this paper explores new methods for quantifying interdisciplinarity in the NSF portfolio.

Suggested Citation

  • Leah G. Nichols, 2014. "A topic model approach to measuring interdisciplinarity at the National Science Foundation," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 741-754, September.
    • Handle: RePEc:spr:scient:v:100:y:2014:i:3:d:10.1007_s11192-014-1319-2
    DOI: 10.1007/s11192-014-1319-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-014-1319-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-014-1319-2?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. Kun Lu & Dietmar Wolfram, 2012. "Measuring author research relatedness: A comparison of word-based, topic-based, and author cocitation approaches," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(10), pages 1973-1986, October.
    2. Ismael Rafols & Martin Meyer, 2010. "Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 263-287, February.
    3. Wagner, Caroline S. & Roessner, J. David & Bobb, Kamau & Klein, Julie Thompson & Boyack, Kevin W. & Keyton, Joann & Rafols, Ismael & Börner, Katy, 2011. "Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature," Journal of Informetrics, Elsevier, vol. 5(1), pages 14-26.
    4. Alan L. Porter & Ismael Rafols, 2009. "Is science becoming more interdisciplinary? Measuring and mapping six research fields over time," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(3), pages 719-745, December.
    5. Loet Leydesdorff, 2007. "Betweenness centrality as an indicator of the interdisciplinarity of scientific journals," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(9), pages 1303-1319, July.
    6. Kun Lu & Dietmar Wolfram, 2012. "Measuring author research relatedness: A comparison of word‐based, topic‐based, and author cocitation approaches," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(10), pages 1973-1986, October.
    Full references (including those not matched with items on IDEAS)

    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. Andrea Bonaccorsi & Nicola Melluso & Francesco Alessandro Massucci, 2022. "Exploring the antecedents of interdisciplinarity at the European Research Council: a topic modeling approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 6961-6991, December.
    2. Su, Hsin-Ning & Moaniba, Igam M., 2017. "Investigating the dynamics of interdisciplinary evolution in technology developments," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 12-23.
    3. Rafols, Ismael & Leydesdorff, Loet & O’Hare, Alice & Nightingale, Paul & Stirling, Andy, 2012. "How journal rankings can suppress interdisciplinary research: A comparison between Innovation Studies and Business & Management," Research Policy, Elsevier, vol. 41(7), pages 1262-1282.
    4. Ricardo Arencibia-Jorge & Rosa Lidia Vega-Almeida & José Luis Jiménez-Andrade & Humberto Carrillo-Calvet, 2022. "Evolutionary stages and multidisciplinary nature of artificial intelligence research," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5139-5158, September.
    5. Alfonso Ávila-Robinson & Cristian Mejia & Shintaro Sengoku, 2021. "Are bibliometric measures consistent with scientists’ perceptions? The case of interdisciplinarity in research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7477-7502, September.
    6. Xuefeng Wang & Zhinan Wang & Ying Huang & Yun Chen & Yi Zhang & Huichao Ren & Rongrong Li & Jinhui Pang, 2017. "Measuring interdisciplinarity of a research system: detecting distinction between publication categories and citation categories," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 2023-2039, June.
    7. Shengli Deng & Sudi Xia, 2020. "Mapping the interdisciplinarity in information behavior research: a quantitative study using diversity measure and co-occurrence analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 489-513, July.
    8. Yi Bu & Mengyang Li & Weiye Gu & Win‐bin Huang, 2021. "Topic diversity: A discipline scheme‐free diversity measurement for journals," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(5), pages 523-539, May.
    9. Giulio Giacomo Cantone, 2024. "How to measure interdisciplinary research? A systemic design for the model of measurement," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(8), pages 4937-4982, August.
    10. Wooseok Jang & Heeyeul Kwon & Yongtae Park & Hakyeon Lee, 2018. "Predicting the degree of interdisciplinarity in academic fields: the case of nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 231-254, July.
    11. Jorge Mañana Rodríguez, 2017. "Disciplinarity and interdisciplinarity in citation and reference dimensions: knowledge importation and exportation taxonomy of journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 617-642, February.
    12. Zuo, Zhiya & Zhao, Kang, 2018. "The more multidisciplinary the better? – The prevalence and interdisciplinarity of research collaborations in multidisciplinary institutions," Journal of Informetrics, Elsevier, vol. 12(3), pages 736-756.
    13. Ran Xu & Navid Ghaffarzadegan, 2018. "Neuroscience bridging scientific disciplines in health: Who builds the bridge, who pays for it?," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 1183-1204, November.
    14. Lina Xu & Steven Dellaportas & Zhiqiang Yang & Jin Wang, 2023. "More on the relationship between interdisciplinary accounting research and citation impact," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(4), pages 4779-4803, December.
    15. Xin Liu & Yi Bu & Ming Li & Jiang Li, 2024. "Monodisciplinary collaboration disrupts science more than multidisciplinary collaboration," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 75(1), pages 59-78, January.
    16. Ballester, Omar & Penner, Orion, 2022. "Robustness, replicability and scalability in topic modelling," Journal of Informetrics, Elsevier, vol. 16(1).
    17. Tanmoy Chakraborty, 2018. "Role of interdisciplinarity in computer sciences: quantification, impact and life trajectory," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1011-1029, March.
    18. Hongyu Zhou & Raf Guns & Tim C. E. Engels, 2022. "Are social sciences becoming more interdisciplinary? Evidence from publications 1960–2014," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(9), pages 1201-1221, September.
    19. Chen, Shiji & Qiu, Junping & Arsenault, Clément & Larivière, Vincent, 2021. "Exploring the interdisciplinarity patterns of highly cited papers," Journal of Informetrics, Elsevier, vol. 15(1).
    20. Ismael Rafols & Alan Porter & Loet Leydesdorff, 2009. "Overlay Maps of Science: a New Tool for Research Policy," SPRU Working Paper Series 179, SPRU - Science Policy Research Unit, University of Sussex Business School.

    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:spr:scient:v:100:y:2014:i:3:d:10.1007_s11192-014-1319-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.