Policy Change, Threat Perception, and Mobility Catalysts: The Trump Administration as Driver of Asylum Migration to Canada
Bibliographic record
Abstract
Almost 60,000 people claimed asylum at Canada's border with the United States between 2017 and 2020, marking Canada's first sustained cross-border asylum migration since the 1990s. Virtually, all entered irregularly via a rural road on the New York/Québec border. The "Roxham Road route" was partly owing to the 2004 Canada/US Safe Third Country Agreement (STCA), which allows both states to refuse asylum-seekers on the grounds that the other offers commensurate protection standards yet only applies to official ports of entry. Roughly, 40 percent of the 60,000 who claimed asylum were US residents with precarious immigration status. This article examines the route's emergence and contributes a novel case on decision-making and destination choices for asylum migration. Data are derived from interviews with over 300 asylum-seekers, two dozen experts, and monthly asylum statistics. The central finding is that Trump-administration immigration policies were the major driver for asylum migration yet do not entirely explain the new route, since a relatively small number of US residents departed for Canada. Interviews revealed that while Trump-era policies fostered a climate of fear, individual experiences with immigration enforcement, loss of temporary protected status, or deferred asylum cases were catalysts for migration. Welcoming Canadian rhetoric and liberal asylum policies were only considered in light of risk in the United States, challenging research findings that asylum-seekers are primarily motivated by destination-state policies. The article also offers qualitative methods for connecting asylum data with migrant decision-making and problematizes the STCA's ethics and effectiveness for managing asylum.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".