Custodial Sanctions and Reoffending: A Meta-Analytic Review
Bibliographic record
Abstract
Beginning in the 1970s, the United States began an experiment in mass imprisonment. Supporters argued that harsh punishments such as imprisonment reduce crime by deterring inmates from reoffending. Skeptics argued that imprisonment may have a criminogenic effect. The skeptics were right. Previous narrative reviews and meta-analyses concluded that the overall effect of imprisonment is null. Based on a much larger meta-analysis of 116 studies, the current analysis shows that custodial sanctions have no effect on reoffending or slightly increase it when compared with the effects of noncustodial sanctions such as probation. This finding is robust regardless of variations in methodological rigor, types of sanctions examined, and sociodemographic characteristics of samples. All sophisticated assessments of the research have independently reached the same conclusion. The null effect of custodial compared with noncustodial sanctions is considered a “criminological fact.” Incarceration cannot be justified on the grounds it affords public safety by decreasing recidivism. Prisons are unlikely to reduce reoffending unless they can be transformed into people-changing institutions on the basis of available evidence on what works organizationally to reform offenders.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.026 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".