Into the woods: Partnering with the Department of Corrections to deliver forestry extension programming
Bibliographic record
Abstract
This paper describes a pilot series of extension programs delivered to inmates within the Mississippi Department of Corrections system. The project was a collaboration between the Chickasaw County Regional Correctional Facility, state Extension forestry specialists, and county Extension staff. A large body of research describes benefits of inmate education, including lower recidivism and reducing tension that could otherwise lead to behavioral problems. Over four months, Mississippi State University Extension personnel worked with prison officials to educate inmates about forestry, logging, and arboriculture. This paper describes the collaborative process driving the project as well as program design, curriculum, materials, and delivery. Besides technical information, instructors discussed opportunities and challenges inmates may encounter if they were to pursue employment in forestry or tree care following their sentences. Instructors worked closely with prison staff to appropriately address teaching in a unique environment and the educational needs of the students. We present the student evaluation process, which was limited by prison rules. Finally, we present results from an evaluation of partners and instructors that elaborate on pitfalls, challenges, and opportunities. The Department of Corrections was highly satisfied with the program and has requested expansion to facilities across the state.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".