Public Attitudes to Sentencing in Canada: Exploring Recent Findings
Bibliographic record
Abstract
This article reports findings from two representative public-opinion surveys that explored Canadians’ attitudes toward three important sentencing issues: the severity of sentencing; the purposes of sentencing; and mandatory sentences of imprisonment. As has been found by polls over the past 30 years, most Canadians believe that sentencing practices are too lenient. The same result emerged from a poll conducted in 2005: 74% of respondents held the view that sentencing is too lenient – a finding consistent with polls conducted throughout the 1980s. With respect to the purposes of sentencing, strongest public support emerged for the restorative sentencing objectives of promoting a sense of responsibility in the offender and securing reparation for the crime victim. There was less support for the more traditional purposes of deterrence and incapacitation. This finding represents a marked contrast to findings from the last survey that evaluated public reaction to sentencing purposes (in 1985). Slightly more than half the sample in 2005 expressed support for mandatory sentencing – a result consistent with opinion surveys from the United States and Australia. However, there was strong public support for mandatory sentencing legislation that also permits a limited degree of judicial discretion. The public appear aware of the dangers of an absolute mandatory sentence of imprisonment and support mandatory sentences in which courts may impose a lesser sentence where exceptional circumstances exist. The implications of these findings for sentencing policy in Canada are discussed.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".