The agricultural prison industry: a scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 The results show many publications focused on the agricultural prison industry were outdated, United States-based, and/or non-original research.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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 it