R. v. Nur: A Positive Step but not the Solution to the Problem of Mandatory Minimums in Canada
Bibliographic record
Abstract
Over the last several decades, Parliament has steadily increased the use of mandatory minimum sentences. Canada now ranks second in the world — behind only the United States — in the number of offences it has that carry mandatory minimums. In R. v. Nur, the Supreme Court of Canada declared unconstitutional the three-year mandatory minimum sentence for a first conviction for possession of a firearm. Prior to Nur, the Court had not struck down a mandatory minimum sentence since R. v. Smith, decided 30 years earlier. In the time between Smith and Nur, the Court was asked to consider the constitutionality of four other mandatory minimum sentences. But in each of these cases the Court upheld the constitutionality of these minimums. Viewed in this context, Nur is a key decision. It represents a critical step towards dismantling a mandatory minimum regime that has gained a foothold in Canada.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".