Regional Assessments Under the Canadian Impact Assessment Act: Objectives, Outcomes and Lessons So Far
Bibliographic record
Abstract
The planning and conduct of regional assessments (RAs) under the Canadian Impact Assessment Act (IAA) has reflected various objectives and planned outcomes. To date, this has included a key focus on improving the effectiveness and efficiency of subsequent project assessments through RA-provided information, analysis and mitigation, although the manner and degree to which these outputs will transfer to and affect the scope of later assessments has yet to be confirmed. Some RAs have also been designed to provide larger effects management and planning outputs, including identifying and recommending broader initiatives for addressing effects and maximizing benefits from future development. RA's potential role in influencing the nature, intensity and distribution of future activities has also been recognized, although this can be challenging where there is no regional planning mechanism for RA to engage with, and especially, given Canadian jurisdictional realities. RAs under the IAA are most likely to be successful in that regard where they are designed and conducted in cooperation with other jurisdictions, and especially, have a direct link to existing and applicable planning processes. Experience also suggests that even where this is the case, there may be challenges if neither process establishes an overall vision for future development, or where there is a lack of specificity in RA outputs or how they are planned to be used in decision-making.
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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 | 0.031 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".