Why and How to Strengthen Indigenous Peoples' Food Systems With Examples From Two Unique Indigenous Communities
Bibliographic record
Abstract
Indigenous Peoples' food systems contain extensive and sophisticated knowledge that is often undocumented and underutilized in contemporary society that has increasingly poor nutrition and loss of food biodiversity. Indigenous Peoples in all global regions are among the most vulnerable to marginalization, food insecurity and chronic disease and will benefit greatly from strengthening their resource-rich food systems to make them more resilient and sustainable. It is in this spirit that we contribute to the databases of Indigenous Peoples' food system knowledge with information on unique traditional foods from the Nuxalk Nation in British Columbia, Canada, and the Pwo Karen People of Sanephong Community, Thailand. Several publications from these case studies originated from interdisciplinary mixed-method research, in part through the United Nations Food and Agriculture Organization. We highlight selected foods with nutrient data and various qualitative and quantitative methods used to identify and promote their use within these unique communities. Our intent is to stimulate complementary strengthening efforts among other traditional and Indigenous Peoples that will contribute to global intercultural food system evidence and advances.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".