Can Circles of Support and Accountability (COSA) Work in the United States? Preliminary Results From a Randomized Experiment in Minnesota
Bibliographic record
Abstract
In 2008, the Minnesota Department of Corrections implemented Minnesota Circles of Support and Accountability (MnCOSA), a sex offender reentry program based on the Circles of Support and Accountability (COSA) model developed in Canada during the 1990s. Using a randomized experimental design, this study evaluates the effectiveness of MnCOSA by conducting a cost-benefit analysis and comparing recidivism outcomes in the MnCOSA (N = 31) and control groups (N = 31). Despite the small total sample size (N = 62), the results from Cox regression models suggest that MnCOSA significantly reduced three of the five recidivism measures examined. By the end of 2011, none of the MnCOSA offenders had been rearrested for a new sex offense compared with one offender in the control group. Because of less recidivism observed among MnCOSA participants, the results from the cost-benefit analysis show the program has produced an estimated US$363,211 in costs avoided to the state, resulting in a benefit of US$11,716 per participant. For every dollar spent on MnCOSA, the program has generated an estimated benefit of US$1.82 (an 82% return on investment).
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 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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".