Rehabilitating futures: Assessing the effects of correctional employment-focused programs on recidivism and employment
Bibliographic record
Abstract
In this paper, I explore the effects of participating in employment-focused programs during incarceration, encompassing job skills and vocational training, on post-release employment and crime outcomes. I develop and estimate a dynamic model of crime, employment, and correctional program participation, using data from serious juvenile offenders in Maricopa County and Philadelphia County. I find that participating in employment-focused programs results in a 4.9%-point increase in employment and a 7.9%-point reduction in crime within three years post-release. These programs facilitate the transition to the legal labor sector and enhance employment stability, mitigating some of the adverse effects of criminal records. They also have a modest impact on preferences towards crime. Furthermore, I show that correctional employment-focused programs significantly affect post-release crime and employment outcomes even if criminal experience has accumulated, and that policies that enhance the impact of such programs on the job-arrival rate can have crucial effects on subsequent crime and employment outcomes.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".