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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".