Mandatory Minimum Sentences of Imprisonment: Exploring the Consequences for the Sentencing Process
Bibliographic record
Abstract
In this article, the author discusses the nature and consequences of the mandatory sentences of imprisonment created by Bill C-63 in 1995. These mandatory sentences constitute the most comprehensive collection of mandatory minima in Canadian history, and will affect significant numbers of offenders. Unlike most mandatory minima created in other jurisdictions such as Australia, England, and Wales, the legislation that created the firearms offence minima offer no provision to be invoked in exceptional cases. In this article, the author addresses the effect that these new statutory minima am likely to have on sentencing patterns It is argued that they should not have an inflationary effect on sentence lengths for all firearms offences, and certainly not for other, unrelated crimes. Allowing the new mandatory minima to inflate sentencing lengths would cause considerable damage to the architecture of the sentencing system. Such a change would also be inconsistent with the codified principles of sentencing. The article concludes by reiterating a proposal to promote a more rational and coherent sentencing policy development: creation of a Permanent Sentencing Commission.
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 | 0.016 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".