Aboriginal Participation in Canadian Environmental Assessment: Gap Analysis and Directions for Scholarly Research
Bibliographic record
Abstract
There has emerged in recent years an increased industry and regulatory demand for the streamlining of environmental assessment (EA), and at the same time, persistent expectations by Aboriginal communities for more effective and meaningful engagement in development decisions. This paper examines the extent to which scholarly research has contributed to solutions for meaningful Aboriginal participation amidst demands for more efficient and shorter timelines for participation and decision-making. Three research priorities are identified from our assessment of peer-reviewed EA scholarly research: the need for empirical-based research assessing the impacts of streamlining on participation and the impacts of meaningful Aboriginal participation on EA efficiencies; the need for better defined scope of issues that should be addressed inside the EA process versus those that are best addressed external to EA; and the need to develop and test alternative mechanisms for Aboriginal participation at the regional and strategic levels, and their contributions to regulatory-based EA decisions.
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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 | 0.084 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".