Bibliographic record
Abstract
The Safe Streets and Communities Act , like many other parts of the government’s crime agenda, relies on both prosecutorial discretion and a general judicial reluctance to strike down mandatory sentences. Successful Charter challenges to mandatory sentences are not impossible, as seen by Molloy J.’s recent decision in Smickle , but they will be difficult. In particular, the use of reasonable hypotheticals in section 12 analysis may be precluded by reliance on the assumption that longer mandatory sentences will not be applied when the Crown has the power to avoid such sentences by electing to prosecute the relevant crime by way of more lenient summary conviction procedures. Courts will then be reluctant to review Crown elections as exercises of prosecutorial discretion. The Supreme Court will ultimately have to decide whether it wishes to main tain the level of judicial deference towards mandatory sen tences that it has demonstrated in the past. This paper argues that a more traditional approach to proportionality that focuses on the relationship between particular crimes and punishment is more promising than newer approaches based on arbitrariness in relation to legislative purposes or gross disproportionality in the costs and benefits of legislative inter ventions, as conducted in the Insite case and Bedford . Following Smith and Ipeelee , a contextual approach to proportionality between crime and punishment that factors in offender cha racteristics should be taken rather than the more abstract approach taken in Morrisey . Should mandatory sentences be found to violate either section 7 or section 12 of the Charter, they will be difficult to justify under section 1. Policy analysis about the necessity and effects of mandatory sentences is best conducted under section 1 rather than within sections 7 and 12.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".