A(nother) New Federal Regime for Assessing Interprovincial Pipeline Projects: The Proposed Impact Assessment Act
Bibliographic record
Abstract
On February 8, 2018, after nearly two years of expert panel and parliamentary committee review, the federal Liberal government tabled Bill C-69, An Act to enact the Impact Assessment Act and the Canadian Energy Regulator Act, to amend the Navigation Protection Act and to make consequential amendments to other Acts. As further set out below, the proposed Impact Assessment Act (IAA) can be described as a bulked-up version of the current Canadian Environmental Assessment Act, 2012 (CEAA, 2012). Like CEAA, 2012, the IAA is built around a designated project list rather than being triggered by federal decision-making generally (as the original Canadian Environmental Assessment Act was). The main differences include a new planning phase where before there was only a screening decision, the elimination of any standing test for public participation, and an expansion of the scope of assessments, including not just environmental effects but also social, economic, and health effects that fall within Parliament’s legislative jurisdiction. The federal government will also have to consider, among other things, a project’s contribution to sustainability and whether it contributes to or hinders Canada’s ability to meet its climate change commitments. This brief paper considers these changes from the perspective of a future inter-provincial pipeline project.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".