BEST PRACTICES FOR PROMOTING PARTICIPATION AND LEARNING FOR SUSTAINABILITY: LESSONS FROM COMMUNITY-BASED ENVIRONMENTAL ASSESSMENT IN KENYA AND TANZANIA
Bibliographic record
Abstract
This paper establishes best practices for community-based environmental assessment (CBEA) in Kenya and Tanzania and examines what participants in community-centered approaches to environmental assessment have learned. Three CBEA cases involving water supply projects were studied using interview methods and action research. Findings show that best practices for encouraging meaningful community involvement include providing access and adequate notice to participants, fairer cost sharing, broader representation of women and youth, participant understanding of the CBEA facilitator and culturally appropriate sharing of findings. Learning outcomes attributable to the CBEA process included technical skills for erosion control, new information about environmental assessment (EA) regulations and shared values of environmental sustainability and community unity. An application of selected best practice approaches in a test case, in order to encourage greater participation and learning, had mixed success. For example, attempts at providing early notice still resulted in it being far too late for most participants and only about one-third of the participants were women. However, a pictograph functioned as an effective tool for reporting CBEA results to the community and demonstrating learning outcomes.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".