Regional & Strategic Assessments in the Proposed Federal Impact Assessment Act (IAA)
Bibliographic record
Abstract
There has been broad agreement among academics, practitioners and stakeholders involved in impact assessment in Canada that regional and strategic assessments offer opportunities to improve the efficiency, effectiveness and fairness of assessment processes and resulting decision making. Among the key benefits are the ability to address broader policy issues, to consider the interaction among a range of past, current and possible future activities, to improve the consideration of alternatives and cumulative effects, to streamline assessments at the project level, and to attract better projects as a result of improved clarity on what types of projects are desired. In spite of its tremendous promise, and endorsement by industry, environmental and indigenous interests alike, implementation in Canada has been slow, and so far, largely ad hoc.\nIn this post, we consider the progress made under Bill C-69 toward the adoption of an effective legislative framework for regional and strategic assessments. We consider this question in three stages. We first summarize the recommendations of the Expert Panel on federal EA reform with respect to regional and strategic assessments. We then consider the changes reflected in Bill C-69. Finally, we conclude with our own assessment of the effectiveness of the proposed changes, and recommend adjustments to ensure meaningful progress on the integration of regional and strategic assessments into the federal assessment process.
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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 | 0.040 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".