Food system sustainability for health and well-being of Indigenous Peoples
Bibliographic record
Abstract
OBJECTIVE: To describe how Indigenous Peoples understand how to enhance use of their food systems to promote sustainability, as demonstrated in several food-based interventions. DESIGN: Comments contributed by partners from case studies of Indigenous Peoples and their food systems attending an international meeting were implemented with public health interventions at the community level in nine countries. SETTING: The Rockefeller Foundation Bellagio Conference Center in Bellagio, Italy, where experiences from case studies of Indigenous Peoples were considered and then conducted in their home communities in rural areas. SUBJECTS: Leaders of the Indigenous Peoples' case studies, their communities and their academic partners. RESULTS: Reported strategies on how to improve use of local food systems in case study communities of Indigenous Peoples. CONCLUSIONS: Indigenous Peoples' reflections on their local food systems should be encouraged and acted upon to protect and promote sustainability of the cultures and ecosystems that derive their food systems. Promoting use of local traditional food biodiversity is an essential driver of food system sustainability for Indigenous Peoples, and contributes to global consciousness for protecting food biodiversity and food system sustainability more broadly. Key lessons learned, key messages and good practices for nutrition and public health practitioners and policy makers are given.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".