Imagining EA 2.0: Outcomes of the 2016 Federal Environmental Assessment Reform Summit
Bibliographic record
Abstract
In May 2016, in anticipation of a formal review of federal environmental assessment processes, experts gathered from across Canada to discuss, weigh options and make recommendations on key issues in federal environmental assessment. It was widely acknowledged that the current regime under the Canadian Environmental Assessment Act, 2012 is broken and needs to be replaced with a visionary new approach comprised of a package of integrated, leading-edge approaches to federal environmental assessment. This paper describes the discussions and outcomes of the Summit, including twelve pillars of a leading-edge environmental assessment regime for Canada. They are:1. Sustainability as a core objective;2. Integrated, tiered assessments starting at the strategic and regional levels;3. Cumulative effects assessments done regionally;4. Collaboration and harmonization;5. Co-governance with Indigenous Nations;6. Climate assessment to achieve Canada’s climate goals;7. Credibility, transparency and accountability throughout;8. Participation for the people;9. Transparent and accessible information flows;10. Ensuring sustainability after the assessment;11. Consideration of the best option from among a range of alternatives; and12. Emphasis on learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".