Overcrowding, Drugs Abuse and Violence vs. Rehabilitation Interviews and Perceptions of a Sample of Prisoners
Bibliographic record
Abstract
Based on the interviews collected and conducted during the data gathering process for the documentary "11 days: within the prison walls”, some inmates from the prison of Nerio Fischione based in Brescia (Italy) decide to share their stories to a microphone and storytellers. The voices provide a sincere look, suitable for investigating the subjective perceptions that inmates have regarding the prison environment, exploring the complexities of their experiences. What emerges from the stories is a place that is far away from the rehabilitation purpose that it should have. The enormous issue of overcrowding, drug pills treatment abuse, and, above all, the pervasive violence within the corridors of the facility. The punitive function seems to prevail and is doubly enacted: firstly, through the nature of the prison itself, and secondly, through the prison conditions that prevent from any possibility to start a positive path. This article aims to contribute to the existing academic debate providing an insight from the prisoners’ perception about their personal experience, focusing on the issues that most frequently emerge from their testimonies: the lack of significance that they experience on a daily basis.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".