Bibliographic record
Abstract
Following recent studies in Florida and Canada, we examine the effects of prison visitation on recidivism among 16,420 offenders released from Minnesota prisons between 2003 and 2007. Using multiple measures of visitation (any visit, total number of visits, visits per month, timing of visits, and number of individual visitors) and recidivism (new offense conviction and technical violation revocation), we found that visitation significantly decreased the risk of recidivism, a result that was robust across all of the Cox regression models that were estimated. The results also showed that visits from siblings, in-laws, fathers, and clergy were the most beneficial in reducing the risk of recidivism, whereas visits from ex-spouses significantly increased the risk. The findings suggest that revising prison visitation policies to make them more “visitor friendly” could yield public safety benefits by helping offenders establish a continuum of social support from prison to the community. We anticipate, however, that revising existing policies would not likely increase visitation to a significant extent among unvisited inmates, who comprised 39% of our sample. Accordingly, we suggest that correctional systems consider allocating greater resources to increase visitation among inmates with little or no social support.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".