Political Rhetoric Meets Legal Reality: How to Move Forward on Free, Prior, and Informed Consent in Canada
Bibliographic record
Abstract
The issue of free, prior, and informed consent (FPIC) from Indigenous communities affected by resource projects has recently received renewed attention in Canada. Indeed, during Canada's 2015 election, Liberal leader Justin Trudeau had gone so far as to say that a “no” from an Indigenous community would assuredly result in a project being cancelled. He soon backed away from this promise by emphasizing only the need for consensus. This report seeks to clarify the meaning behind FPIC and what it would entail to implement it in Canadian law. Legal realities, both within Canada and internationally with the FPIC concept in the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) itself, should be taken into account. Canada already has a judicially-developed duty to consult framework, which in certain circumstances requires something close to consent, such as when the rights in question are particularly strong or when consent is substituted for consultation, such as via negotiations with third-parties like resource developers. Equally important, consultation currently applies to a much wider range of circumstances than is reasonably possible under consent. By narrowing this scope, adapting consent into this legal framework could therefore actually reduce Aboriginal and treaty rights for Indigenous communities. A more careful reading of UNDRIP also shows that FPIC does not require consent for a project to proceed, but instead requires meaningful good faith efforts to obtain consent. It is also only applicable to a narrower range of circumstances to lands that are owned by Indigenous communities, as opposed to those that have asserted claims. On this issue, Canada already meets or exceeds UNDRIP’s requirements for FPIC – by applying the duty to consult framework over lands over which there are asserted claims, and by applying something close to consent in certain circumstances. Operationalizing FPIC in the Canadian context would pose important, and often forgotten, challenges. First, it would require extensive foundational work in various sectors, including identifying the extent of its applicability on various projects and whose consent is ultimately required, especially in cases where there are internal divisions within an Indigenous community or multiple communities with divergent views. Second, Canada has a federal system of government, and the provinces and territories actually have jurisdiction over many areas that would be affected by FPIC, such as natural resources. Operationalizing FPIC would therefore require a decentralized approach in Canada.
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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 | 0.104 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.055 | 0.062 |
| Scholarly communication | 0.039 | 0.021 |
| Open science | 0.009 | 0.019 |
| Research integrity | 0.025 | 0.039 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".