Bibliographic record
Abstract
Introduction The development of EIA commitments in international law has occurred against a backdrop of normative arrangements existing in domestic and international legal settings. EIA as a distinct form of public decision-making was first developed under US federal law as part of the National Environmental Policy Act (NEPA). Subsequently, EIA processes were developed by a number of US states, and in the mid-1970s countries such as Canada, France, Australia and New Zealand developed their own EIA processes. Since the 1970s, the adoption of EIA legislation has grown steadily throughout the world, and it is now estimated that over 100 countries have EIA legislation. EIA norms have not only spread horizontally to other states, but they have also spread vertically, influencing the development of EIA norms in international law and within international organizations. The globalization of EIA commitments has not, however, been a one-way projection of domestic environmental policy into a transnational setting. The reception and development of EIA commitments by other states in both their domestic and international decision-making processes has also been influenced by general principles of international environmental law, such as the principle of nondiscrimination, the duty to prevent transboundary harm and the duty to cooperate with other states to preserve and protect the natural environment. Latterly, the constellation of principles surrounding sustainable development that has become embedded in transnational environmental governance structures has also influenced the development of EIA processes in transnational legal settings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".