Can Recidivism be Prevented from Behind Bars? Evidence from a Behavioral Program
Bibliographic record
Abstract
Incarcerated offenders are offered a wide range of programs to encourage their chances of successful reintegration into society. Little is known, however, about the degree to which such programs improve prisoners' reentry. In this paper, I study the effects of a cognitive-behavioral program implemented in Quebec, Canada, with a rich micro-level dataset. To manage the econometric issue of inmates' self-selection into the program, I exploit inmates' random assignment to probation officers who exhibit varying propensities to recommend the rehabilitation measure. I find large, negative, and significant effects of the program on recidivism, as measured by an inmate's probability of serving a subsequent sentence: within one year following release, the program reduces recidivism by up to 18 percentage points. Moreover, the program is shown to decrease the number of future offenses. Further analyses indicate that the most plausible mechanism can be attributed to the program's success in altering offenders' preferences towards crime.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".