Proportionality in the Criminal Law: The Differing American versus Canadian Approaches to Punishment
Bibliographic record
Abstract
The focus of this Article shall be upon the Eighth Amendment of the United States Constitution and s. 12 of the Canadian Charter of Rights and Freedoms, both of which prohibit “cruel and unusual punishment”; and their effect on mandatory criminal sentencing (via penal statute) in the two countries. The Article shall begin by briefly explain the differences between the jurisdictional application of criminal justice in the United States and Canada. The Article will next present and explain the American Eighth Amendment approach to the constitutionality of mandatory criminal sentencing and contrast this to the Canadian s. 12 approach to the constitutionality of mandatory criminal sentencing. The contrasting of the two national approaches will underlie the main argument of the Article, namely that if one’s concern is the fair and proportionate application of justice, then the Canadian approach to reconciling the constitutional prohibition against “cruel and unusual punishment” and the application (through penal statute) of mandatory criminal sentencing is the superior one. The Article shall conclude with a discussion of the possible reasons for the differing national approaches to mandatory criminal sentencing.
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 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".