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 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 | 0.046 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".