One Amazon, One Health: Understanding Asháninka People’s Perspectives to Health and Well-Being in Response to Epidemic Threats, Like COVID-19
Bibliographic record
Abstract
Considering the vulnerability of Indigenous communities to increasing epidemic threats, like the COVID-19 pandemic, understanding how the Asháninka People of the Pichis Valley of Peru experience and understand health and well-being has become of paramount importance. This case study describes the process by which Indigenous health and well-being are being addressed as well as some of the preliminary findings and activities that were born out of this process, which include intercultural health dialogues and traditional seed exchange workshops. This project is being carried out by a local Peruvian NGO that has had a long-term presence in the region as well as the democratically elected Indigenous federation that is the recognized governing authority for the Asháninka People of the Pichis Valley. While this project is still ongoing, there have already been many lessons learned about the importance of using a multidisciplinary research team, that included Indigenous co-researchers, and taking on a holistic One Health approach.
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How this classification was reachedexpand
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".