First steps toward best practice SEA in a developing nation: lessons from the central Namib uranium rush SEA
Bibliographic record
Abstract
The intent of this study is to contribute to the discussion of strategic environmental assessment (SEA) best practice based on experience gained in a recent SEA initiative: the central Namib (Namibia) uranium rush SEA. We evaluate this SEA process against internationally established characteristics of ‘best practice’ SEA to improve and strengthen future practice in Namibia. The study draws primarily on the final assessment report as well as inputs from six informants involved in the assessment. The results reveal some elements of good practice as well as areas for improvement, and in particular, the need for improved baseline data collection; adequate consideration of alternatives; committing to preferred scenario/options; enforceability; and a more robust institutional capacity. We offer insight into how consideration of these factors may help to strengthen SEA practice in Namibia. Overall, the SEA may not represent a ‘best practice’ example according to international standards, but it does suggest a potentially bright future for SEA practice in Namibia.
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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 | 0.001 |
| 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".