Applying next-generation community-based environmental assessment: case studies from Kenya
Bibliographic record
Abstract
The Next-Generation Community-Based Environmental Assessment (NG-CBEA) advances a comprehensive framework by integrating key components of sustainability, public participation, follow-up and monitoring, and learning to support sustainable community development initiatives in low-income countries. This paper aims to demonstrate the application of the NG-CBEA framework to two proposed irrigation cases in Kenya, identifying key barriers and enablers that emerged from this application. Methodologically, the framework emphasized qualitative, participatory approaches, and the cases met Kenyan regulatory requirements, resulting in the approval of formal EA licenses. Key enablers identified through participant feedback included defining sustainability locally, early and ongoing participation, shared responsibility for follow-up and monitoring, and effective communication for learning. Barriers experienced in the NG-CBEA application included the time required to implement a comprehensive sustainability approach, limited access to information for meaningful public participation, absence of traditional knowledge in follow-up and monitoring, and logistical challenges for field visits to enable learning. Overall, the cases demonstrate the robustness of the framework for advancing next-generation components in CBEA and achieving more sustainable 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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 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".