Offender Diversion Into Substance Use Disorder Treatment: The Economic Impact of California’s Proposition 36
Bibliographic record
Abstract
OBJECTIVES: We determined the costs and savings attributable to the California Substance Abuse and Crime Prevention Act (SACPA), which mandated probation or continued parole with substance abuse treatment in lieu of incarceration for adult offenders convicted of nonviolent drug offenses and probation and parole violators. METHODS: We used individually linked, population-level administrative data to define intervention and control cohorts of offenders meeting SACPA eligibility criteria. Using multivariate difference-in-differences analysis, we estimated the effect of SACPA implementation on the total and domain-specific costs to state and county governments, controlling for fixed individual and county characteristics and changes in crime at the county level. RESULTS: The additional costs of treatment were more than offset by savings in other domains, primarily in the costs of incarceration. We estimated the statewide policy effect as an adjusted savings of $2317 (95% confidence interval = $1905, $2730) per offender over a 30-month postconviction period. SACPA implementation resulted in greater incremental cost savings for Blacks and Hispanics, who had markedly higher rates of conviction and incarceration. CONCLUSIONS: The monetary benefits to government exceeded the additional costs of SACPA implementation and provision of treatment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".