Establishing a community of practice of researchers, practitioners, policy-makers and communities to sustainably manage environmental health risks in Ecuador
Bibliographic record
Abstract
The Sustainably Managing Environmental Health Risk in Ecuador project was launched in 2004 as a partnership linking a large Canadian university with leading Cuban and Mexican institutes to strengthen the capacities of four Ecuadorian universities for leading community-based learning and research in areas as diverse as pesticide poisoning, dengue control, water and sanitation, and disaster preparedness. By 2009, train-the-trainer project initiation involved 27 participatory action research Master’s theses in 15 communities where 1200 community learners participated in the implementation of associated interventions. This led to establishment of innovative Ecuadorian-led master’s and doctoral programs, and a Population Health Observatory on Collective Health, Environment and Society for the Andean region based at the Universidad Andina Simon Bolivar. Building on this network, numerous initiatives were begun, such as an internationally funded research project to strengthen dengue control in the coastal community of Machala, and establishment of a local community eco-health centre focusing on determinants of health near Cuenca. Alliances of academic and non-academic partners from the South and North provide a promising orientation for learning together about ways of addressing negative trends of development. Assessing the impacts and sustainability of such processes, however, requires longer term monitoring of results and related challenges.
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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 | 0.075 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".