Gender roles, food system biodiversity, and food security in Indigenous Peoples' communities
Bibliographic record
Abstract
Traditional knowledge and practice of Indigenous Peoples related to their food use and well-being is a wealth of information for academic study and for public health nutrition. Despite unique long-evolved heritages of knowledge of ecosystem resources, Indigenous Peoples comprise 15% of the global poor, but only 5% of the world's population, and they experience poverty, discrimination, and poor nutritional health at far greater rates than mainstream populations in their nations of residence. These disparities are unacceptable in all human rights frameworks, and the call to alleviate them resonates through all human development programmes and the United Nations organizations. The scholars contributing to this special issue of Maternal and Child Nutrition describe how gender roles and the right to food for several cultures of Indigenous Peoples can be fostered to protect their unique foods and traditions, providing food sovereignty and food and nutrition security benefits, especially for women and children. Aspects of societal maternal or paternal lineality and locality, division of labour, spirituality and decision-making are described. These factors structure the impact of gender roles with Indigenous worldviews on the dynamics of family food access, its availability and use, and the use of local food biodiversity. Cultures of Indigenous Peoples in Ecuador, Nigeria, Thailand, India, Canada, Japan, and Morocco are discussed. This publication is a work of the Task Force on Traditional, Indigenous and Cultural Food and Nutrition of the International Union of Nutritional Sciences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".