A unified LLM-assisted framework for extracting and classifying geographical study areas from article metadata

Geographical study areas (GSAs) anchor empirical research to specific locations and are essential for geographically aware knowledge organization, retrieval and spatial meta-analysis. However, GSA information is rarely stored in structured form in bibliographic databases and instead appears as unstructured text in article titles and abstracts, hindering large-scale spatial analyses of scientific knowledge production. This study proposes an LLM-assisted unified framework to systematically extract, disambiguate and classify multidimensional GSA information from large-scale article metadata. The proposed method follows an ‘Expert–Teacher–Student’ framework. First, a dual-dimensional GSA taxonomy integrating spatial scale and spatial attributes was constructed through expert–LLM collaboration. Second, a retrieval-augmented annotation pipeline generated high-quality supervision data by combining LLM ensemble reasoning with external geospatial knowledge verification. Third, a lightweight unified model was developed via parameter-efficient fine-tuning to jointly perform GSA extraction and classification, reducing annotation costs and mitigating error propagation. Experiments demonstrate strong performance with high computational efficiency. Applying the framework to 163,781 geography-related articles (2010–2024) reveals significant research attention–population mismatch, epistemic biases and scale disparities in global knowledge production. The proposed framework advances geographically aware literature mining and provides a scalable foundation for spatial bibliometrics and GIScience.