Food sovereignty, health, and produce prescription programs: A case study in two rural tribal communities
Bibliographic record
Abstract
Structural inequities contribute to food systems in which tribal communities in the U.S. are more likely to experience barriers to healthy food access, including financial barriers, lack of geographic proximity, or both. Food sovereignty movements improve food access by shifting power to local people to build food systems that support cultural, social, economic, and environmental needs. Financial incentive programs, including produce prescription programs, have emerged as a promising intervention to improve food access and support food sovereignty. This case study describes the implementation of two federally funded produce prescription programs (Produce Prescription Projects or PPR) under the U.S. Department of Agriculture (USDA) Gus Schumacher Nutrition Incentive Program (GusNIP) in two rural tribal communities: the Yukon Kuskokwim Delta region in Alaska, and the Navajo Nation, which spans parts of New Mexico, Arizona, and Utah. We illustrate how PPR can be tailored to accommodate local and diverse cultures, strengthen community power, and be uniquely suited for the challenges of increasing access to nutritious food in rural tribal communities. We also highlight recommendations and future areas of research that may be useful for other rural tribal communities implementing PPR.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".