SEA, Urban Plans and Solid Waste Management in Kenya: Participation and Learning for Sustainable Cities
Bibliographic record
Abstract
This paper examines the role of participation and learning in Kenyan strategic environmental assessments (SEAs) of urban plans that include a solid waste management (SWM) component. Two SEA cases were studied using 40 semi-structured interviews and two focus groups. Data are analysed qualitatively employing NVivo software. Participation is assessed using ideal conditions of learning derived from Transformative Learning Theory, and operationalised for this study. Strengths of SEA participation are freedom from coercion and equal opportunity to participate. Notable weaknesses include inaccessibility of SEA documents, inadequate participant funding, and lack of feedback and transparency about the SEA findings. Participants exhibited numerous learning outcomes and associated social actions on urban SWM including waste sorting, recycling and composting (instrumental learning), sharing values and community collaborations on cleanup and recycling (communicative learning), and altering conventional viewpoints from ‘waste for disposal’ to ‘waste as a resource’ (transformative learning), including for livelihood opportunities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".