Achieving Next Generation Environmental Impact Assessment Follow-up and Monitoring
Bibliographic record
Abstract
Despite growing scrutiny of Environmental Impact Assessment (EIA) in Canada and worldwide, the follow-up and monitoring component remains under practiced, leaving EIA decision-makers and practitioners with little understanding of the accuracy of impact predictions made and the effectiveness of mitigation measures developed during the EIA project-planning phase. The Minister’s Expert Panel further highlighted the importance of enhancing follow-up and monitoring during the recent review of EIA processes in Canada. The research identifies six leading edge practices for next generation EIA follow-up and monitoring: public and Indigenous participation, continuous learning, clear roles and responsibilities, independent oversight, adaptive management and traditional knowledge. Approaches to implement those practices in a Canadian context are explored and supported by guidance that captures the learning potential of EIA follow-up and monitoring. The six practices are intended as a package and are presented with practical guidance for proponents, regulators, consultants and others involved in EIA.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".