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Rehabilitating futures: Assessing the effects of correctional employment-focused programs on recidivism and employment

2025· article· en· W4406756281 on OpenAlexafffund
Maria Antonella Mancino

Bibliographic record

VenueEuropean Economic Review · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council
KeywordsRecidivismFutures contractEconomicsActuarial scienceLabour economicsFinancial economicsPsychologyCriminology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.336
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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