BEST PRACTICES FOR PROMOTING PARTICIPATION AND LEARNING FOR SUSTAINABILITY: LESSONS FROM COMMUNITY-BASED ENVIRONMENTAL ASSESSMENT IN KENYA AND TANZANIA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it