The New Canadian Law of Refugee Exclusion: An Empirical Analysis of International Criminal Law Deportation Orders, January 2018 to July 2020
Bibliographic record
Abstract
Abstract Perpetrators of war crimes and crimes against humanity, and senior officials in notorious government regimes, can be deported from Canada. This study reports on the first complete and systematic empirical analysis of all finalized international criminality deportation cases in Canada. The analysis, a review of deportation cases finalized between January 2018 and July 2020, shows that Canada is using deportation law in place of, and instead of, refugee exclusion law. This means that scholars interested in Canadian refugee exclusion should play close attention to deportation law. This study also found that international criminality allegations were usually made against people for their involvement in problematic police, prison, or military institutions. Most international criminality deportation investigations were minimal and revolved almost entirely around a person’s self-disclosures. This article concludes with a discussion about how deportation law and process makes international criminal law unique in the deportation context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".