The Transformative Potential of Indigenous-Driven Approaches to Implementing Free, Prior and Informed Consent: Lessons from Two Canadian Cases
Bibliographic record
Abstract
While it is increasingly recognised as a core element of the emerging international Indigenous rights regime, the implementation of the principle of free, prior and informed consent (fpic) remains contested. As the comparative literature shows, if and how fpic is implemented depend both on the institutional context and on the agency of actors involved. Faced with deep power asymmetries and strong institutional resistance to their understanding of fpic as a decision-making right, a number of Indigenous groups in Canada have taken advantage of the uncertain legal context to unilaterally operationalise fpic through the development of their own decision-making mechanisms. Building on two case studies, a mining policy adopted by the Cree Nation of James Bay and a community-driven impact assessment process established by the Squamish Nation, this article argues Indigenous-driven mechanisms can be powerful instruments to shape how fpic is defined and translated in practice.
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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 | 0.035 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.025 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".