Indigenous-led struggles for health justice in the context of the climate emergency: insights from Guatemala
Bibliographic record
Abstract
This practice paper reflects on an ongoing Participatory Action Research project that combines community-engaged methods, national data analysis and advocacy to support community-based emergency response to extreme weather events in 16 Indigenous communities in Alta Verapaz province, Guatemala. Our work points to a worrying predicament experienced in climate-affected areas, where some populations face a dangerous confluence of climate vulnerability, social exclusion and state abandonment that imperils human health. Indigenous communities in Alta Verapaz are often particularly vulnerable to health impacts from climate-driven extreme weather events, a reality compounded by the historical and contemporary ways the state marginalises them. We share work from our project activities to shed light on these interconnected problems and how Indigenous communities in Alta Verapaz, especially Maya Q'eqchi' communities, are using creative strategies to confront them. Technical solutions are important but insufficient responses. Community-led activism to push for state support to address extreme weather events, as has been practised in struggles for health rights, can provide vital tools for addressing the increasing challenges these populations face in the context of the climate crisis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".