The agricultural prison industry: a scoping review
Bibliographic record
Abstract
Prison farms are common programs within correctional services; however, knowledge is limited regarding the agricultural prison industry. As a starting point for further study and policy development, we conducted a scoping review to map knowledge on the industry. The results show many publications focused on the agricultural prison industry were outdated, United States-based, and/or non-original research. Findings reveal agricultural positions tend to be filled by prisoners with pre-existing work skills and relatively low support needs and agricultural positions are not necessarily driven by market demands. Findings also show prisoners experience a lack of workplace protections, such as workers’ compensation, the ability to unionize, and adequate workplace safety and hazardous materials training. Yet, a purported benefit of agricultural programs was improved food security for prisoners. Other finds show there is a predominant focus on self-sufficiency and cost-savings for prisons in the face of inadequate or worsening budgets but limited available data quantifies relationship, prison farms shift from being rehabilitative-focused to profit-driven over a certain amount of acres. We conclude by identifying gaps in the literature on the agricultural prison industry and listing areas of future inquiry.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".