Indigenous food systems: contributions to sustainable food systems and sustainable diets.
Bibliographic record
Abstract
Indigenous food systems are remarkable reservoirs of unique cultural knowledge grounded in historical legacy and spirituality that acknowledge the inextricable link of people with their sustainably managed resources. These sustainable food systems can provide essential understanding about sustainable diets and their importance to many of the Sustainable Development Goals. Unique practices of land and plant and animal management are now threatened by extreme weather and overall climate variability that compound the risks of a long list of environmental assaults upon indigenous lands. Despite vast knowledge of the world's territories and guardianship of 80% of global species diversity, indigenous peoples experience extreme disparities with greater population obesity, undernutrition and micronutrient malnutrition, as well as other health gaps that are grounded in poverty and marginalization. This contributes to the inability of many indigenous peoples to realize sustainable diets known with traditional knowledge. Indigenous food system knowledge is incorporated in both cultivated and wild foods, synergies with the natural environment and biodiversity, adaptation to local conditions and knowledge how these conditions are changing, light carbon footprints, and minimal use of external inputs as fuel and environmentally sensitive technologies. Indigenous food systems across the world demand recognition and protection for their valuable knowledge not only for the benefit of populations of the knowledge holders, but as part of the collective global heritage. Governments, universities, research centers, and United Nations agencies must make Indigenous food systems a priority in their work to document the scientific and cultural benefits of these resources, and to promote more sustainable food systems and, with them, to develop more sustainable global diets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".