Jurisdictional Cooperation Under the Proposed Federal Impact Assessment Act (IAA)
Bibliographic record
Abstract
Constitutional responsibility over environmental issues is shared between the federal and provincial governments in Canada. As a result, efforts to ensure effective cooperation have been important to the successful implementation of environmental law and policy. With respect to impact assessments, the issue is more complex. Jurisdiction, in the context of federal impact assessment (IA) reform, is about the relationship between the federal IA process and processes in provincial, municipal, Indigenous and international jurisdictions potentially affected by proposed projects, policies, plans or programs. The focus of discussion about IA jurisdictional cooperation is often on the relationship between federal and provincial assessment processes, but it is important to consider the role of other jurisdictions. The issue is further complicated by the need to consider not just project assessments, but also regional and strategic assessments.\nIn this post, we consider the progress made under Bill C-69 toward the adoption of an effective legislative framework for cooperative impact assessments. We consider this question in three stages. We first summarize the recommendations of the Expert Panel on federal reform with respect to cooperation and harmonization with other jurisdictions and processes. We then consider the changes reflected in Bill C-69 compared to the current process under CEAA 2012. Finally, we conclude with our assessment of the effectiveness of the proposed changes and recommend adjustments to ensure meaningful progress toward effective, efficient and fair cooperative assessment processes.
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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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".