Factors That Facilitated Learning Through a Central American Community-Based Pest Management Project: An Exploration Of Non-Formal Educational Practice
Bibliographic record
Abstract
Food security is essential for human well-being. Intending to improve human and environmental health, increase agricultural productivity, and reduce poverty in Costa Rica, Nicaragua and Honduras, the Canadian International Development Agency's (CIDA) Community-based pest management in Central American agriculture project focused on developing programmes and policies that could impact local, national and regional agricultural practices regarding the handling, storage and use of pesticides. In international development initiatives, such as this one, participatory approaches to governance provide opportunities for learning through public engagement in decision-making processes. This longitudinal qualitative case study examines what processes, activities and factors enabled, and/or constrained learning from participation in this CIDA project. Findings reveal what learning-focused, meaningful participatory approaches to governance look like in practice. Results show that learning occurred through strategic-level planning and implementation of project activities and through opportunities to experiment with newly acquired knowledge and skills. Other considerations included: (a) clearly establishing learning goals, (b) understanding learners' characteristics, and (c) creating effective pedagogical approaches for learners. Policy and practical implications are explored. Keywords: Central America agriculture, learning for sustainability, public participation, rural development, transformative learning
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".