Wrongful Extradition: Reforming the Committal Phase of Canada’s Extradition Law
Bibliographic record
Abstract
There has recently been an upswing in interest around extradition in Canada, particularly in light of the high-profile and troubling case of Hassan Diab who was extradited to France on the basis of what turned out to be an ill-founded case. Diab’s case highlights some of the problems with Canada’s Extradition Act and proceedings thereunder. This paper argues that the “committal stage” of extradition proceedings, involving a judicial hearing into the basis of the requesting state’s case, is unfair and may not be compliant with the Charter and that the manner in which the Crown conducts these proceedings contributes to this unfairness. It also argues that regardless of the Act’s constitutionality, in light of Diab and other disturbing cases, the time is ripe for law reform to ensure that extradition proceedings are carried out in a way that is consistent with Canadian public policy. Some suggestions for reform are made, as well as a proposal for a serious Parliamentary effort.
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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 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".