Conditional Sentencing and the Perspectives of Crime Victims: A Socio-Legal Analysis
Bibliographic record
Abstract
Over thepastdecade there hasbeen an expansion in the use ofcommunity-based alternatives to imprisonment in most Western nations. In Canada this has resulted in the use of more conditional sentences. While the benefits of this type of sentence are apparent from the perspective of stateresources and offenders, little isknown abouttheirimpacton victims. This article seeks tofill thevoid. .After analyzing developments in Canadian case law on conditional sentencing since R. v. Proulx, the authors present the results of a study of the perceptions offemale victims of personal injury offences, Crown counsel, and victims' advocates. In particular, the authors examine thevictims'reactions to conditional sentences generally; theextentof theirknowledge of the sanctions imposed in their cases specifically; theirsatisfaction with attempts to obtain their input for sentencing submissions; and their views on the efficacy of specific conditions that are commonly imposed in the courts and on the efficacy ofenforcement generally. The authors conclude with suggestions on how to improvetheconditional sentencing process from the·perspectiiJe of victims. They recommend that victims should be better informed of the content of the condition order, the reasons for sentence, any incidents of breach while the offender is carrying out the sentence, and itsfinal outcome. They also recommend that judges bemorewillingto imposefinancial reparations asa condition ofsentence.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".