分析加拿大科研资助18年数据，发现AI研究迅速崛起，性别与地域影响显著。
Decoding Funded Research: Comparative Analysis of Topic Models and Uncovering the Effect of Gender and Geographic Location
- 用BERTopic等三种模型分析科研项目，识别出更细粒度的研究主题。
- 发现人工智能等新兴领域快速扩张，不同省份有明显研究专长差异。
- 提出新算法COFFEE，首次实现BERTopic的性别与地域影响分析。
优化国家科研投入需明晰研究趋势及人口与地理因素的影响，尤其在推动公平、多元与包容的背景下。本研究基于加拿大自然科学与工程研究理事会（NSERC）2005至2022年资助的科研项目，系统比较了三种主题建模方法：隐含狄利克雷分布（LDA）、结构化主题模型（STM）和BERTopic。同时提出一种名为COFFEE的新算法，用于增强BERTopic对协变量的稳健分析能力，弥补其原生缺乏协变量分析功能的缺陷。结果表明，尽管三类模型均能有效划分核心科学领域，但BERTopic始终识别出更精细、连贯且具前瞻性的主题，如人工智能的快速扩展。借助COFFEE进行协变量分析，证实各省份存在明显研究专长，并揭示跨学科中持续存在的性别主题模式。这些发现为资助机构制定更公平、有效的资助策略提供了坚实的实证基础，有助于提升科研生态的整体效能。
原文摘要 · Abstract (English)
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.
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