Youth Recidivism: A Qualitative Study of Risk and Resilience
Bibliographic record
Abstract
Abstract\n \nThe rates of reoffending for Ontario youth are high and come at a significant cost to both society and the youths involved. Research to date has explored risk and protective factors. Despite this progress, the relationships between these factors and recidivism are not well understood. Knowing that a youth is exposed to any of these identified risk or protective factors does little to explain why these factors do not affect all youth equally and why some youths reoffend while others do not. Resilience theory has increasingly been used as the framework to explore the concept of recidivism. The present study investigates what makes youths successful in not reoffending and explores the ways in which they are resilient. A qualitative methodology involving in-depth interviews offered participants the opportunity to offer their own perspectives. Data were generated from ten youth participants who were residing in a secure custody facility in Ontario at the time of the study. The findings highlighted the complexity of factors that influence whether a given youth will offend and/or reoffend or not. The experiences of the ten youths in this study demonstrated that many of those influences were external and in particular structural or societal level barriers. The suggestion has been made that both the study of recidivism and interventions with at-risk youth would benefit from further enhancement of resilience theory through the inclusion of societal context and the incorporation of structural and cultural violence perspectives.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".