Indigenous knowledge and leadership for climate change adaptation in nutrition
Bibliographic record
Abstract
Indigenous knowledge and leadership for climate change adaptation in nutritionClimate change adaptation to support our food and nutrition system will berequired to achieve sustainable development, especially in combating hunger (SDG 2) while also achieving health and mitigating climate change (SDG 3 and 13) [1].Climate change repercussions for nutrition are especially concerning in countries where essential needs remain unmet [2].For Indigenous and non-Indigenous scientists working in Latin America (LA), this means that we need to embrace positive transformationsto shape our future.Persistent health challenges that are embedded in social and structural inequities(e.g.prevalent infectious diseases [3], and high levels of under nutrition and anaemia [4]) are at risk of being exacerbated by climate change.For example,anaemia prevalence ranges from 16% to 86%, among young Indigenous children, with stunting and overweight affecting up to 48% and 40%, respectively of children in LA [4,5].Promisingly, the good news for LA countries,where up to 8% of its population self-identifies as Indigenous [6], is that we can recognise, preserve, and leverage the insights and knowledge of Indigenous Peoples.A reciprocal collaboration is essential to inform adaptation policies that ensureimproved nutrition for Indigenous communities whilst simultaneously supporting the sustainable development of countries by reducing harm to the environment, preserving cultures, and strengthening well-being and income opportunities for Indigenous Peoples.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".