Reducing criminal recidivism in Alaska: The Set Free Model
Bibliographic record
Abstract
Crime associated with problematic substance use remains a defining characteristic in the United States criminal justice system. In Alaska, a perennial leader in US criminal recidivism rates, thousands of formerly incarcerated individuals continue to commit crimes and misuse drugs and alcohol following their release from incarceration. The total cost of these crimes to victims and Alaska’s criminal justice system is over $2.3 billion annually. The Set Free Model is a comprehensive intervention addressing the primary risk factors of criminal recidivism within an innovative therapeutic campus environment. Occurring within a four-phase operational framework for an average of 6 to 18 months, participants engage in a suite of services proven to reduce further criminal recidivism. These services include certified peer support, supportive housing, co-occurring substance use disorder treatment, career placement, intensive case management, and positive community reintegration. Over an 18-month period, the nonprofit treatment agency Set Free Alaska provided the Set Free Model to a sample of 32 formerly incarcerated adults at high risk of criminal recidivism. Participants displayed a 21.8% recidivism rate compared with the current rate of 66.4%. Treatment engagement rates significantly improved compared with traditional outpatient rates (94.7% vs. 66.7%). Employment rates were also remarkable compared with national employment rates at 1-year postrelease (100% vs. 37%). Validated calculations indicate the sample population may achieve $6.25 million in cost savings and net economic benefits. Evaluation results indicate the model possesses significant potential to reduce criminal recidivism and should be further expanded and evaluated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".