The impact of incarceration on reoffending: A period-to-period analysis of Canadian youth followed into adulthood
Bibliographic record
Abstract
Several theories and policies on punishment describe within-person processes whereby an increase in the number of days a person spends incarcerated decreases their likelihood of reoffending. Contradicting these perspectives, meta-analyses report universal consensus that incarceration has either a null or crime-inducing impact on reoffending. However, studies included in this meta-analytic work relied on between-group analyses. Within-person analyses more closely align with how theories and policies describe the relationship between incarceration and reoffending and have the additional benefit of addressing the selection bias problem of between-group analyses. Using longitudinal data from the Incarcerated Serious and Violent Young Offender Study in British Columbia, Canada (n = 1719), a first-differenced fixed-effect estimator modeled the relationship between year-over-year change in the number of days spent incarcerated and future year-over-year change in number of convictions. Between ages 12â25, year-over-year increases in days spent incarcerated prospectively influenced year-over-year decreases in convictions. This finding was consistent across types of convictions, age-stages, ethnicity, gender, birth cohort, and exposure to different youth justice legislation. It is unclear whether reductions in convictions resulted from incarceration having a deterrent effect or a rehabilitative effect. It would be a mistake to interpret findings as support for expanding the use of incarceration or that Canada's correctional system should maintain the status quo.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".