Interweaving Indigenous and Settler Knowledges for Environmental Protection in Resource Development and Indigenous Conciliation in Canada
Bibliographic record
Abstract
Canada has prioritized “reconciliation” with Indigenous peoples just as debates over the ecological impacts of extractive industries are rising in volume and vigour in the public sphere. While apparently distinct, these two substantial and pressing priorities—and their underlying harms of colonialism and extractivism—can be seen as intertwined issues of social, economic, and environmental justice and sustainability. Thus, interweaving—more than “integrating” or “bridging” as employed in the literature—Indigenous ways of living and knowing and Euro-Canadian knowledge systems in resource development projects stands to help Canadians advance both environmental protection and respect for First Peoples and their traditionally used and claimed territories. Building on these premises, this study by “a White person of consciousness” examines the needs, benefits, obligations, frameworks, and challenges of involving Indigenous ways of knowing in the current framework for approving and managing resource development projects in Canada. It notes imperatives and efforts to link Indigenous and settler systems of “knowledge” in such projects and lessons learned and suggests best practices for policies and practices aimed at encouraging both environmental protection and Indigenous conciliation, which can further each other. Finally, the study considers implications of such interweaving for Canada’s international standing.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.018 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".