Community Engagement in Environmental Assessment for Resource Development: Benefits, Emerging Concerns, Opportunities for Improvement
Bibliographic record
Abstract
This paper discusses contemporary issues surrounding the efficiency of environmental assessment (EA) and the effectiveness of community engagement with focus on Canadian practice in the last two decades. Based on a review of the EA literature, we provide a brief overview of the benefits of effective engagement in EA processes. We then identify and discuss three enduring challenges to effective engagement amidst increasing pressures for a more efficient EA process, namely capacity, streamlining of EA processes, and the timing of EA and engagement in the resource development process. The paper concludes with key recommendations to ensure community engagement as a platform for enhancing increased inclusivity in environmental decision making. The paper is part of a special collection of brief discussion papers presented at the 2014 Walleye Seminar held in Northern Saskatchewan, which explored consultation and engagement with northern communities and stakeholders in resource development.
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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 | 0.048 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".