A Patchwork of Promises: Implementing Free, Prior, Informed Consent in Canadian and British Columbia Impact Assessments
Bibliographic record
Abstract
Indigenous peoples’ rights are often disproportionately impacted by resource extraction projects, with inadequate consultation and benefit-sharing from these projects. Since Canada’s endorsement of the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP), the federal government and the province of British Columbia (BC) have passed legislative measures to align domestic law with UNDRIP. Impact assessments are an important vehicle for the implementation of UNDRIP, particularly the principle of free, prior and informed consent (FPIC). Through a qualitative assessment of six mining projects being assessed under the federal Impact Assessment Act, 2019 and the BC Environmental Assessment Act, 2018, this thesis scrutinizes how FPIC has been operationalized under these two laws. Key findings include that FPIC implementation occurs along a spectrum ranging from “soft consent” to more robust forms of “hard consent”. Differentiating factors include the degree of joint decision-making afforded to Indigenous communities, the accountability mechanisms applied when consent is withheld, and the dispute-resolution processes used to achieve consensus. It also finds that several legislative and legal instruments have been employed by assessment agencies to implement FPIC. Finally, it identifies emerging patterns in Indigenous communities being able to negotiate Impact and Benefit Sharing Agreements (IBAs) from a stronger negotiating position as legal frameworks move towards consent-based mechanisms.
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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 | 0.075 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.049 | 0.028 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".