Bibliographic record
Abstract
This chapter provides an overview of domestic EIA law from a comparative perspective. The discussion is framed in light of differing theoretical models for EIA that emphasize in varying degrees the scientific, political and normative aspects of assessment as a means to explain how EIA processes affect outcomes. Viewed comparatively, different jurisdictions do not so much privilege one model over another, but rather emphasize and respond in varying ways to these elements. Importantly, each of these elements carries with it legitimating function, and the presence of multiple elements suggests an interaction whereby each of these elements potentially compensates for deficiencies in the others. As the shortcomings of scientific prediction, particularly in a time of increasingly rapid global environmental change, make outcomes less certain, there is greater room for both political and normative influences within EA processes. EA processes push decision makers towards a certain form of politics premised on open, participatory and justificatory procedures, which will vary in their compatibility with the underlying institutional structures of implementing jurisdictions. The analysis looks at long established EIA syatems in North America and Europe, as well as emerging systems in China, South Africa and the “state environmental review” system in Russia. Reference is also made to a number of transnational EIA systems, such as those found in development banks and established under international treaties.
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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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".