A community-based intervention to build community harmony in an Indigenous Guatemalan Mining Town
Bibliographic record
Abstract
The presence of large-scale mining operations poses many threats to communities. In a rural community in Guatemala, community leaders were motivated to address divisiveness and local conflict that have been exacerbated since the arrival of a mining company in the region. Prior research by our team identified spiritual and cultural strengths as important sources of strength and resilience in the community. We piloted a community-based intervention centred on spiritual and cultural practices in the region, to address divisiveness and build community harmony. One hundred and seventeen participants from over 18 villages in the municipality participated in the workshops and follow-up focus groups. Community leaders facilitated the intervention and partnered with the academic researcher throughout the research process. Overall, community members and facilitators expressed satisfaction with the workshop. Further, our analysis revealed three important processes important to the development of community harmony in the region: (a) mutual recognition and collectivisation; (b) affirmation of ancestral roots and connections to Mother Earth and (c) inspiring action and momentum towards solutions. These mechanisms, and the socio-political contexts that undermine them, have important implications for how global health programmes are developed and how collective processes for well-being are understood within an inequitable, conflict-laden world.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".