From Participant to Partner: Applying Indigenous Understandings of Treaties to Canada’s Environmental Impact Assessment Processes
Bibliographic record
Abstract
The following seeks to explore solutions forward amid increasing pressure to improve the quality of Indigenous involvement within environmental assessments (EAs). This paper describes the historical entanglements of resource development, colonialism, and limited recognition of Indigenous interests within EAs currently. It deconstructs the implications of the following: extractive methodologies habitually used within EAs; distinctions between Canadian and Indigenous legal systems; cultural variances in perceptions of power structures; and noticeable systemic issues within EA processes. Drawing from Indigenous understandings of treaties, this article brings forth some key considerations necessary to establishing meaningful Indigenous involvement during EAs. It positions treaties as a powerful, practical orientation towards envisioning a framework that utilizes practices which foster genuine collaboration and dialogue amongst all parties involved. To this end, this article contends with the importance of addressing gaps in quality of Indigenous involvement during EAs, particularly as calls for reconciliation and sustainable environmental decision-making continue.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.047 | 0.048 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".