Decoding Funded Research: Comparative Analysis of Topic Models and Uncovering the Effect of Gender and Geographic Location
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| Format: | Preprint |
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2025
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| _version_ | 1866918165096693760 |
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| author | Kafiabad, Shirin Tavakoli Schiffauerova, Andrea Ebadi, Ashkan |
| author_facet | Kafiabad, Shirin Tavakoli Schiffauerova, Andrea Ebadi, Ashkan |
| contents | Optimizing national scientific investment requires a clear understanding of evolving research trends and the demographic and geographical forces shaping them, particularly in light of commitments to equity, diversity, and inclusion. This study addresses this need by analyzing 18 years (2005-2022) of research proposals funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). We conducted a comprehensive comparative evaluation of three topic modelling approaches: Latent Dirichlet Allocation (LDA), Structural Topic Modelling (STM), and BERTopic. We also introduced a novel algorithm, named COFFEE, designed to enable robust covariate effect estimation for BERTopic. This advancement addresses a significant gap, as BERTopic lacks a native function for covariate analysis, unlike the probabilistic STM. Our findings highlight that while all models effectively delineate core scientific domains, BERTopic outperformed by consistently identifying more granular, coherent, and emergent themes, such as the rapid expansion of artificial intelligence. Additionally, the covariate analysis, powered by COFFEE, confirmed distinct provincial research specializations and revealed consistent gender-based thematic patterns across various scientific disciplines. These insights offer a robust empirical foundation for funding organizations to formulate more equitable and impactful funding strategies, thereby enhancing the effectiveness of the scientific ecosystem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18803 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decoding Funded Research: Comparative Analysis of Topic Models and Uncovering the Effect of Gender and Geographic Location Kafiabad, Shirin Tavakoli Schiffauerova, Andrea Ebadi, Ashkan Artificial Intelligence Machine Learning Optimizing national scientific investment requires a clear understanding of evolving research trends and the demographic and geographical forces shaping them, particularly in light of commitments to equity, diversity, and inclusion. This study addresses this need by analyzing 18 years (2005-2022) of research proposals funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). We conducted a comprehensive comparative evaluation of three topic modelling approaches: Latent Dirichlet Allocation (LDA), Structural Topic Modelling (STM), and BERTopic. We also introduced a novel algorithm, named COFFEE, designed to enable robust covariate effect estimation for BERTopic. This advancement addresses a significant gap, as BERTopic lacks a native function for covariate analysis, unlike the probabilistic STM. Our findings highlight that while all models effectively delineate core scientific domains, BERTopic outperformed by consistently identifying more granular, coherent, and emergent themes, such as the rapid expansion of artificial intelligence. Additionally, the covariate analysis, powered by COFFEE, confirmed distinct provincial research specializations and revealed consistent gender-based thematic patterns across various scientific disciplines. These insights offer a robust empirical foundation for funding organizations to formulate more equitable and impactful funding strategies, thereby enhancing the effectiveness of the scientific ecosystem. |
| title | Decoding Funded Research: Comparative Analysis of Topic Models and Uncovering the Effect of Gender and Geographic Location |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.18803 |