Participatory Rural Appraisal Approaches for Public Participation in EIA: Lessons from South Africa
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
Public participation in environmental impact assessment (EIA) often falls short of the requirements of best practice in the move towards sustainable development, particularly for disadvantaged and marginalized communities. This paper explores the value of a participatory rural appraisal (PRA) approach for improved public participation in a sample of EIA’s for photovoltaic projects in South Africa. PRA was conducted post facto making use of selected PRA tools. Findings show that a great deal more information was obtained by the PRA approach, confirming the perceived weakness of traditional PP for vulnerable and disadvantaged communities. It is concluded that a PRA approach has considerable potential for improving meaningful public participation, which should improve EIA, build capacity in those communities, and enhance livelihoods and sustainable resource use.
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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 | 0.001 |
| 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