Serious and Violent Young Offenders’ Decisions to Recidivate: An Assessment of Five Sentencing Models
Bibliographic record
Abstract
Five models of sentencing were assessed with respect to their impact on the decisions of young offenders to recidivate. The five sentencing models tested were fairness, deterrence, chronic offender lifestyle, special needs, and procedural rights. A sample of 400 incarcerated young offenders from the Vancouver, British Columbia, metropolitan area were asked questions regarding their attitudes toward these sentencing models and their intentions to recidivate after serving a period of incarceration. Principal components analyses suggested that although these models do not function independently, two composite models do shed some light on the issues that young offenders consider when contemplating their decisions and intentions to recidivate. Despite the ability of these models to predict half of the explained variance in young offenders’ decisions regarding recidivism, a majority of the sample appeared to not be affected exclusively by cost-benefit analysis, punishment, or reintegrative motivations. The authors conclude that without additional variables and even higher predictive validity, it is premature for policy makers to focus on any single model of sentencing in constructing juvenile justice laws.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".