Shared Opportunities on Institutional Lands
Bibliographic record
Abstract
AIM: This article outlines preliminary findings of a 3-year project that explored on-site food production on institutional properties, primarily healthcare facilities. BACKGROUND: There are growing pressures on healthcare facilities to improve their food offerings and incorporate food gardens into their health programs. While several healthcare facilities produce food on-site, there are few studies that explore opportunities, capacities, and institutional barriers related to on-site food production. METHODS: The study employed mixed methods including historical review, case studies, surveys, interviews, pilot garden projects, and Geographic Information System mapping. The number of participating institutions varied by method. RESULTS: Benefits associated with on-site food production can be health, economic, environmental, and social. There are also institutional barriers including administrative roadblocks, perceived obstacles, and the difficulty in quantitatively, measuring the qualitatively documented benefits. CONCLUSIONS: The benefits of food gardens far outweigh the challenges. On-site food production has tremendous potential to improve nutrition for staff and patients, offer healing spaces, better connect institutions with the communities in which they are located, and provide the long-professed benefits of gardening for all involved-from therapeutic benefits and outdoor physical activities to developing skills and social relationships in ways that few other activities do.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 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".