Bibliographic record
Abstract
The process of environmental impact assessment (EIA) was introduced for the first time in the United States in 1969 under the National Environmental Policy Act for all major federal activities. Since then, there has been an everwidening acceptance, particularly by the industrialised nations of the world, of the view that environmental effects likely to be caused by a proposed development are material considerations within any planning decision-making process. The influence of the US federal measures led to the rapid incorporation of EIA into state and local statutes across that country and then by the government of Canada in 1973. Many other developed countries followed including, Australia at commonwealth level (1974), Japan (1984) and New Zealand (1991). Although a number of its member countries, such as France and Ireland, had embraced EIAs as early as 1976, followed by the Netherlands (1981), the Council of Environmental Ministers of the European Communities did not adopt a Directive on EIAs for certain types of development until 1985. Their implementation became mandatory in 1988 ( Montz and Dixon 1993 ; Sanchez 1993 ; Geraghty 1996 ). As for developing countries, while many of the 121 sovereign states that might be so categorised had, by the 1990s, at least considered EIA legislation, only nineteen had put in place the necessary administrative, institutional and procedural frameworks for the implementation of EIA systems, only six of which were successfully operational ( Ebisemiju 1993 ).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.014 |
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".