Indigenous Peoples: Traditional knowledges, climate change, and health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Indigenous Peoples around the globe make up approximately six percent of the global population, yet they sustainably care for around eighty percent of the world's remaining biodiversity. Despite continued political, economic, and racial marginalization, as well as some of the worst health inequities on the planet, Indigenous Peoples have worked hard to maintain their cultures and languages against all odds. Indigenous Peoples' close connections to land, water, and ecosystems, however, have placed them at increasing vulnerability from the effects of climate change. With this, the health risks from climate change have unique considerations within Indigenous Nations for both mitigation and adaptation responses that are largely unappreciated. This Indigenous narrative review will synthesis the current climate and health landscape of Indigenous Peoples at a global, high-level scale, including relevant international mechanisms and considerations for Indigenous Peoples' health. This Indigenous narrative review will also explore and reflect on the strengths of Indigenous traditional knowledges as it pertains to climate change and health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it