Bibliographic record
Abstract
Introduction In light of the background norms examined in Chapters 2 and 3, this chapter and the next provide a detailed examination of the international commitments to conduct EIAs themselves. While a principal purpose in these chapters is to describe the commitments themselves as a matter of positive law, this task is undertaken with several additional objectives in mind. First, I want to explore the relative impact of the different background norms on the development of international EIA commitments. The purpose in tracing the relationship of the EIA commitments to the normative influences previously discussed goes beyond merely describing the evolution or development of these commitments, but is also informed by the idea that these different influences will impact the structure and meaning of the commitments themselves. For example, the extent to which international EIA commitments are underlain by substantive considerations, in addition to procedural ones, has implications for the role of EIA. Secondly, by looking across different international contexts where EIA commitments form part of the overall approach to protecting the natural environment, we can draw some tentative conclusions as to why EIA commitments have become prevalent within international environmental governance structures, the type of problems EIAs are being called upon to address and the factors which are contributing to the formation of EIA commitments.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".