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Immigration Law, Stay of Removal, Interlocutory Orders, Judicial Analytics, Empirical Legal Studies, Computational Law, Artificial Intelligence, Machine Learning, Large Language Models, Federal Court, Canada


This article examines decision‐making in Federal Court of Canada immigration law applications for stays of removal, focusing on how the rates at which stays are granted depend on which judge decides the case. The article deploys a form of computational natural language processing, using a large‐language model machine learning process (GPT‐3) to extract data from online Federal Court dockets. The article reviews patterns in outcomes in thousands of stay of removal applications identified through this process and reveals a wide range in stay grant rates across many judges. The article argues that the Federal Court should take measures to encourage more consistency in stay decision‐making and cautions against relying heavily on stays of removal to ensure that deportation complies with constitutional procedural justice protections. The article is also a demonstration of how machine learning can be used to pursue empirical legal research projects that would have been cost‐prohibitive or technically challenging only a few years ago – and shows how technology that is increasingly used to enhance the power of the state at the expense of marginalized migrants can instead be used to scrutinize legal decision‐making in the immigration law field, hopefully in ways that enhance the rights of migrants. The article also contributes to the broader field of computational legal research in Canada by making available to other non‐commercial researchers the code used for the project, as well as a large dataset of Federal Court dockets.


"Refugee Law Lab Working Paper"