Towards a home-grown approach to strategic environmental assessment: adapting practice and participation in Kenya
Bibliographic record
Abstract
Kenya is among the first countries in sub-Saharan Africa to formally require the assessment of public policies, plans and programs. National strategic environmental assessment (SEA) guidelines were finalized in 2012 to adapt and enhance SEA practice in the Kenyan context. The purpose of this research was to examine recent Kenyan SEA, with a particular emphasis on public participation, by developing and applying an analysis framework that both incorporates commonly accepted SEA principles and approaches and is cognizant of the national context in shaping SEA practice. Results reveal that a number of SEA practices are consistent with the framework in the nine cases considered, such as containing standard SEA components and developing monitoring plans. A requirement that each SEA must include educating the public about SEA is an innovative local adaptation. Results also show a number of practices that are still emerging, such as initiating SEA early and disseminating results to the participants. We conclude that SEA is still developing in Kenya, but processes for conducting it are slowly adapting to the Kenyan context.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".