The Constitutional Stopping ofMotor Vehicles in Canada andthe United States:A Comparative Analysis
Bibliographic record
Abstract
When can the police constitutionally stop a motor vehicle in the United States of America and Canada? In this column, I intend to examine this question through a comparative analysis that considers the law in both countries. As will be seen, the power of the police to stop a motor vehicle in Canada is significantly greater than the power of the police to do so in the United States. I intend to illustrate this proposition by considering the most recent decision of the Supreme Court of the United States on this issue (Kanas v. Glover, 140 S.Ct. 1183 (2020)), and then the law in Canada. I will also consider how Glover would likely have been decided in Canada. Finally, I will end by considering how the issue of “racial profiling” has affected the stopping of motorists by the police in Canada. Let us start with the facts in Glover.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".