The Role of Science in Contemporary Canadian Environmental Decision Making: The Example of Environmental Assessment
Bibliographic record
Abstract
In this article, we examine the role of science in Canada’s federal environmental assessment (EA) regime to illustrate opportunities for improvement. We do not address the application of science in EA practices (i.e., how to do good science within EA processes), which has been thoroughly reviewed by others. Instead, we examine the context for science in EA law: we examine the components of a regulatory regime, enshrined by law, that would allow for scientifically defensible assessments and evidence-based decision making. We have four objectives: (1) to provide a recent history of the role(s) of science in Canada’s legislated EA regimes, including public support for science in EA law; (2) to propose five components necessary in an EA regime to ensure strong inclusion of science; (3) to evaluate if new proposed legislation meets scientific standards for modern EA, particularly cumulative effects and climate change; and (4) to encourage collaboration between scholars and practitioners in law and the natural and social sciences to work towards stronger scientific foundations in Canada’s EA regimes at all levels.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.032 | 0.055 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".