From "Guideline Order" to "Impact Assessment": The Evolution of Federal Environmental Assessment Legislation in Canada
Bibliographic record
Abstract
Federal and provincial governments across Canada have enacted comprehensive environmental assessment processes to evaluate the benefits and burdens of significant proposed infrastructure and resource activities. In recent years, federal processes have become a focal point for jurisdictional tensions, including conflicts over the regulation of major projects, natural resource development, and greenhouse gas emissions. In the wake of the Supreme Court of Canada’s landmark 2023 reference opinion in Reference re Impact Assessment Act, this article follows the evolution of federal environmental impact legislation from its inception during the 1980s to the impugned legislation. Beginning with the development of the Environmental Assessment and Review Process Guidelines Order and its subsequent 1992 legal challenge at the Supreme Court in Friends of the Oldman River Society v. Canada (Minister of Transport), we provide a high-level overview of the successive legal and procedural frameworks governing environmental assessment in Canada. Special attention is given to jurisdictional issues considered in the 2023 Supreme Court reference opinion and anticipated amendments to the present iteration of the governing legislation.
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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 | 0.028 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".