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A unified LLM-assisted framework for extracting and classifying geographical study areas from article metadata
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DOI：10.1080/13658816.2026.2686261.png)
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Abstract
En 中文
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.
Keywords:
Geographical study area
large language models
geographical information retrieval
scientific metadata
geospatial artificial intelligence
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