Re-Imagining Indigenous Peoples’ Role in Natural Resource Development Decision-Making: Implementing Free, Prior and Informed Consent in Canada Through Indigenous Legal Traditions
Bibliographic record
Abstract
Indigenous communities, non-governmental organizations, and industry stakeholders across Canada are calling for a new form of government review of major natural resource development projects, one where governments must obtain the Free, Prior and Informed Consent of Indigenous peoples before approving any projects affecting their traditional territories. At the same time, Indigenous, academic, legal, and professional communities are leading a resurgence of Indigenous legal traditions. Together, the two movements offer a powerful opportunity for reconciliation. This opportunity is potentially bolstered by the 2014 Supreme Court decision in Tsilhqot’in Nation v. British Columbia, which emphasized the importance of securing consent from Indigenous communities in specific circumstances. The status quo of government review of natural resource projects has evoked serious and sustained criticism from Indigenous peoples who submit that their perspectives, their rights, and their concerns are not adequately addressed or protected by the current process. Using the Enbridge Northern Gateway Project as a case study of the pitfalls of the status quo of government review, I explain why the federal government should implement a Free, Prior and Informed Consent regime in Canada. I draw on human rights, environmental justice, and economic arguments to support the case for implementation. For such a Free, Prior and Informed Consent regime to be most effective it must incorporate Indigenous legal traditions, empowering every Indigenous community to engage with its own legal traditions and define for itself the meaning of Free, Prior and Informed Consent. The tools, scholarship, and practical lessons emerging from the renaissance of Indigenous law can facilitate this implementation.
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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 | 0.049 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.051 | 0.085 |
| Scholarly communication | 0.027 | 0.009 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".