Mapping Studies on Unauthorized Immigration in the International Migration Review: Results from Large-Language Models

Many states around the world create an unprotected class of migrants by legally categorizing them as “unauthorized”. Yet, we have a limited understanding of the state of knowledge that has resulted from this empirical phenomenon, particularly outside the U.S. and over time. In this article, we map the state of knowledge on unauthorized migration by analyzing the last 30 years of papers published in a leading migration journal. Articles were identified through a comprehensive keyword-based search strategy and analyzed using a computational pipeline that combines natural language processing and large language model-assisted classification. Our findings reveal a persistent empirical emphasis on Mexico–U.S. migration, with economic drivers and disparities, as well as immigration laws and policies, dominating articles’ content. Our analysis also identifies underexplored or peripheral topics, including studies on gender or the environment, highlighting the need for more diversified, cross-national research. Ultimately, by providing a detailed account of our computational mapping approach, we hope this study will serve as a blueprint for future scholars to track how migration research evolves into the future.