Bibliographic record
Abstract
Indigenous Peoples globally experience extreme poverty, marginalization and vulnerability which make them among the worst for nutrition and health in any country. However, the wealth of traditional knowledge embedded in food systems can contribute to creation of effective nutrition promotion programs for food security. Our work documented local food systems in 10 groups of Indigenous Peoples living in rural settings in 8 countries (Canada, Peru, Federated States of Micronesia, Kenya, Nigeria, Colombia, India, Thailand) and used 24‐hr dietary data to determine % energy derived from local, often unique foods in contrast to market foods with high energy contents and low nutrient density. Numbers of locally available food species varied from 32 (Maasai, Kenya) to 240 (Pohnpeian, Micronesia); access to market food contributed from 5% to 90% of total adult dietary energy. Diversity in local food species correlated positively with several nutrients and dietary quality. Stunting in children was up to 50% often without low weight/height; adult BMIs varied with obesity prevalence highest with high % energy from market food. In developing nutrition and health interventions for Indigenous Peoples local indigenous food resources need to be incorporated into many activities, with consideration of physical activity and improved knowledge and access to quality market food. (Supported by the CIHR, IAPH and INMD, and FAO)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".