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 12–25, 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. • A first-differenced fixed-effect estimator helped address selection bias issues in research on incarceration and reoffending. • Year-over-year increases in days spent incarcerated prospectively influenced year-over-year decreases in convictions. • Findings were consistent when stratifying the sample in various ways (e.g., across gender, ethnicity, birth cohort). • It would be a mistake to interpret findings as support for expanding the use of incarceration or maintaining the status quo.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".