Abstract The ability of migration scholars to synthesize knowledge is increasingly hindered by the rapid expansion of the field, both in sheer volume and interdisciplinarity. This article introduces LlaMig (large language model for migration research), an open-source, local framework designed to transform massive scholarly text into a structured, queryable database. By fine-tuning the Llama 3.2 3B architecture, LlaMig demonstrates a transformative leap in classification accuracy. Applied to a corpus of 22 267 articles retrieved from the Web of Science, LlaMig effectively detected 13.3% of traditional keyword search results that were irrelevant to human mobility. Substantively, the model uncovered critical research gaps in the ‘climate-migration nexus’: while research volume is expanding exponentially, 72% of studies treat climate change as a broad, composite driver, largely ignoring hazards that are critical for human health and ecological well-being such as biodiversity loss, pollution and infectious diseases. Functioning as a pre-processing engine for full-scale systematic reviews, LlaMig empowers researchers to navigate massive textual data faster without sacrificing qualitative depth. This framework offers a scalable, ethical solution for accelerating discovery in migration research while maintaining rigorous standards of data security and privacy.
Iacus et al. (Wed,) studied this question.