IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-032-23282-3_39.html

Mapping the Landscape: Gender and Research Funding in Canada Through the Lens of LLMs and Topic Modeling

Author

Listed:
  • Shirin Tavakoli Kafiabad

    (Concordia University, Concordia Institute for Information Systems Engineering)

  • Andrea Schiffauerova

    (Concordia University, Concordia Institute for Information Systems Engineering)

  • Ashkan Ebadi

    (Concordia University, Concordia Institute for Information Systems Engineering
    National Research Council Canada)

Abstract

Understanding the evolving landscape of scientific research is critical for ensuring fair and effective resource allocation, particularly in large-scale government funding programs. This study analyses historical scientific research proposals in Canada to identify trends and patterns, aiding in equitable resource allocation. We used various topic modelling techniques, including Latent Dirichlet Allocation (LDA) and Structural Topic Models (STM), and evaluated their performance against a large language model (LLM), specifically BERTopic. Our investigation focused on the coherence, interpretability, and relevance of the topics each model extracted. The results show that the BERTopic model is superior at generating distinctive and meaningful topics. Using this optimal model, we then analysed how research focus and funding have changed over time in relation to the gender of the researchers. Our findings demonstrate that the BERT-based approach provides more accurate insights and is better able to reveal the influence of gender on funding distribution.

Suggested Citation

  • Shirin Tavakoli Kafiabad & Andrea Schiffauerova & Ashkan Ebadi, 2026. "Mapping the Landscape: Gender and Research Funding in Canada Through the Lens of LLMs and Topic Modeling," Springer Proceedings in Business and Economics,, Springer.
    • Handle: RePEc:spr:prbchp:978-3-032-23282-3_39
    DOI: 10.1007/978-3-032-23282-3_39
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:prbchp:978-3-032-23282-3_39. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.