The Implementation of Free, Prior and Informed Consent in Canada: Different mechanisms in the Canadian treaty landscape
Bibliographic record
Abstract
This research is intended to contribute to the current conversation in Canada on the implementation of the United Nations Declaration on the Rights of Indigenous Peoples by providing an overview of what different actors across Canada are currently doing to implement ‘free, prior and informed consent’ (FPIC). Even though the concept of FPIC is found in several places in the UN Declaration, this study looks specifically at its application in relation to natural resource development projects and how it is implemented in Indigenous communities that have varying treaty relationships with their traditional lands. Thematic analysis was the primary method used to answer the research question ‘are there differences in how FPIC is operationalized with Indigenous groups that have modern treaties when compared to areas in Canada where Indigenous peoples have historical numbered treaties or no treaties at all?’ The academic literature specifically related to FPIC and addressing ‘consent’ was coded for themes and analyzed for cases of FPIC implementation in relation to natural resource development. These cases were categorized into types of mechanisms as well as whether they were primarily driven by government, industry or Indigenous peoples. The cases were then placed in a table to look for patterns in the choice of approach. Based on the limited sample of cases found in the literature, there does not appear to be any difference in how Indigenous communities, governments or industry choose to implement the principle of FPIC based on the presence or absence of treaties. There was no identifiable trend between the signing of a treaty and the mechanisms used to operationalize FPIC. The range of cases analyzed demonstrates that mechanisms can be found through a variety of approaches and there is no single mechanism that is optimal. Given the diverse legal traditions of Indigenous peoples in Canada, any attempt to create a standardized approach would be inappropriate. Indigenous consent to development projects should not be a yes or no question being asked by governments to Indigenous peoples but rather a process that builds relationships to help to guide the decision-making process. There is no singular tool for the implementation of FPIC but rather a combination of vehicles that are adapted to the local circumstances. Indigenous consent is ideally a relational process and not a decision made at a single point in time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".