Revisiting ‘who gets in’: borders and migration management in the era of automation and AI in Canada
Bibliographic record
Abstract
This article examines the transformation of borders with the integration of Automated Decision Support Systems, including Artificial Intelligence, in Canada's migration management since 2015. This study pursues the dual objectives of analyzing the introduction of ADSS and its impact on decision-making regarding immigration applications. The research focuses on the Chinook system, introduced in 2018 for temporary immigration files, and explores how these technologies influence the discretion of visa officers, who are the ultimate decision-makers. Through an in-depth content analysis of governmental documents, many obtained via Access to Information requests, this study provides unique insights into the use of automation and artificial intelligence in immigration application processing and decision-making. Through a fine grain analysis, this article argues that Automated Decision Support Systems significantly alter the process of determining 'who gets in' by transforming the exercise of human discretion, therefore reconfiguring the confines of borders. The conclusion discusses how this new era of Canada's migration management has profound human and legal consequences that call for the need to balance technological advancements with the preservation of substantive decision-making processes to protect procedural fairness and access to justice for immigration candidates who appeal a refusal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.024 | 0.030 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".