The effects of prison program participation on recidivism of ex-offenders in Mississippi
Bibliographic record
Abstract
Correctional education research strongly suggests that an increase in inmates' education will reduce recidivism rates. This study utilized logistic regression techniques to investigate the effects of prison education program participation on recidivism and employment rates. Using this method made it possible to conclude that inmates who participated in prison intervention/educational programs were significantly less likely to recidivate. The purpose of this study was to identify to what extent the Mississippi Department of Corrections' (MDOC's) intervention/educational programs reduce recidivism. The pre-existing data used were historical information collected as part of a longitudinal study on Mississippi inmates since 2000. The data were transferred every quarter to the National Strategic Planning and Analysis Research Center (nSPARC) for management and analysis. Initial tests found that several variables had a relationship with recidivism. The findings in this study suggest that ex-offenders who completed an education/vocational program or completed a counseling program were 87% ( p < 0.001), 9.9% (p < 0.005), respectively, less likely to recidivate than those ex-offenders who did not participate in any type of education or intervention program. The results also suggest that ex-offenders who enrolled in but did not complete an education/vocational program were 10% (p < 0.005) less likely to recidivate than those ex-offenders who did not participate in any type of education or intervention program. Recommendations that result from these findings include an increase in the number and quality of intervention/educational programs in Mississippi prisons. Policies could be suggested and/or implemented that would reduce the number of people who violate the law upon their re-entry into society.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".