“The Biggest Problem With You…”: Racial Profiling and Canada’s Program of Extra-Territorial Migrant Interdiction
Bibliographic record
Abstract
On April 3, 2019, Andrea and Attila Kiss tried to board an Air Canada Rouge flight from Budapest to Toronto. Andrea’s sister was ailing, and the couple planned to visit Canada for two months to support her family. Their travel was legitimate and lawful. Their documents were in order. But when they lined up to check in, Andrea made a mental note of a fact that was about to become relevant: as members of the Hungarian Roma community, they were the only racialized people in line. Andrea and Attila did not reach the check-in counter. They were stopped and pulled out of line by a private security guard. They were questioned, their documents were photographed, and—minutes later—a Canadian immigration official forbade the airline from allowing them aboard the plane. And so, the only racialized people in line trying to get to Canada were profiled, turned around, and sent home without even a ticket refund. Later, they found out the official reason for their deboarding: Canada thought that there were enough “indicators” to conclude that they were not planning on staying temporarily, but permanently. How did this work legally? Exploring that question is this paper's purpose. We show how a little examined tool—the Electronic Travel Authorization—enables the overseas screening of travellers to Canada. Using evidence unearthed in litigation, we show how this tool is used to racially profile travellers and trace the origins of the program. Then, we ask a simple question, is any of this legal?
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.055 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".