The Canadian Federal Environmental Assessment Framework, as illustrated by the Canadian Environmental Assessment Agency and the National Energy Board
Bibliographic record
Abstract
In this presentation we provide an overview of the Canadian national environmental assessment regime, with particular emphasis on practical aspects relating to public and Indigenous consultation, and participation. We will also review an illustrative case study where there was transboundary interest, highlighting how the transboundary dialogue and engagement actually played out in a concrete example. The Government of Canada is committed to ensuring its federal environmental assessment processes: provide robust oversight and thorough environmental assessments of areas under federal jurisdiction, while also working with provinces and territories to avoid duplication; ensure that decisions are based on science, facts, and evidence, and serve the public’s interest; provide ways for the public to express views and opportunities for experts to meaningfully participate; and require project advocates to choose the best technologies available to reduce environmental impacts. The purpose of the case study portion of this presentation is to examine a Canadian project to illustrate how the environmental assessment process identified/examined transboundary impacts, how the EA process addressed public concerns related to the cross-border impacts and how these concerns weighed into the outcome of the project’s development. By examining a Canadian example that incorporated and considered transboundary input, we hope to provide greater insight into how transboundary impacts in projects have been analyzed and how the public can engage in the environmental 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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".