Prison Pups: Assessing the Effects of Dog Training Programs in Correctional Facilities
Bibliographic record
Abstract
During the past twenty-five years, the number of prison programs in which inmates train dogs has increased rapidly. There are no comprehensive data on the prevalence of such programs, but they are in existence in at least twenty U.S. states, Canada, Australia, New Zealand, and Italy. Though extremely popular among both administrators and inmates, we have only anecdotal accounts to assess the effects of dog training by inmates. Such programs appear to have the potential to break down barriers of fear and mistrust between staff and inmates; and there is also some evidence, again anecdotal, that they reduce recidivism and behavioral infractions among inmates. Literally no systematic studies exist, however. This research provides preliminary information from data collected in two Kansas prisons (a men's and a women's institution) in which inmates train assistance dogs and dogs made available for adoption by the general public. This paper focuses on the qualitative findings from the interviews conducted at the men's prison, and examines motivations for entering the program, challenges inmates face in their work, and the benefits they believe come participating.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".