Leisure education as a rehabilitative tool for youth in incarceration settings.
Bibliographic record
Abstract
In 1870, the first National Prison Association Congress passed a resolution identifying the goal of prisons to be rehabilitation of inmates. Prior to that time, the primary function of prisons was considered to be punishment for those who committed crimes, and protection for society. Beginning in the late nineteenth century, and carrying through the twentieth century, the role of recreation within prisons has often been debated. While some feel that prisons should be for punishment and therefore there should be no recreation made available, others considered the primary function of recreation programs to be a means of increasing productivity and controlling behavioral problems. Some felt that recreation was necessary in order to occupy the minds of inmates, thus crowding out the evil thoughts that motivated criminal activity, while others considered recreation to be an essential tool through which individuals' creative and expressive talents could be developed (Byers, 1884; Codding, 1911; Johnson, 1989). Regardless of the motive, by the turn of the century, many correctional institutions provided access to a wide variety of recreation activities including sport, music, drama, and special interest clubs. Yet, as we enter the twenty first century, there are still many who feel that recreation services have no role to play within the walls of prisons.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.007 | 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".