Le Pays Des Canadas: A Policy Analysis of Free, Prior and Informed Consent
Bibliographic record
Abstract
The consultation process for large-scale natural resource projects has been plagued by a number of critical issues that have ultimately disrupted major sources of revenue for Canada. Regulatory bodies perform the bulk of the consultation work on behalf of the Canadian public. Yet their efforts to properly engage with Indigenous rights and title have been mired by the politicization of the engagement process. Indigenous peoples in Canada are demanding that their collective rights be recognized in negotiations over resource projects. Proponents, who must also engage Indigenous peoples, are demanding that government do more to fulfill its duty to consult prior to engagement. Riding a wave of goodwill after being elected in 2015, the Liberal government reiterated its promise to begin a new relationship with Indigenous peoples. Part of that mandate includes implementing the United Nations Declaration on the Rights of Indigenous Peoples. Free, prior and informed consent (FPIC) is included in a number of the articles as a necessary requirement for observing indigenous peoples’ right to self-determination. In Canada, the term ‘consent’ has caused much debate over its meaning and the power over territory it potentially cedes to the title holders. This Capstone provides a background of the Declaration and analyzes how free, prior and informed consent can be implemented in Canada. It demonstrates that consent is already in use through a variety of means and that when viewed as a long-term reconciliation process, the federal government can lead the country by focusing on the principles of FPIC rather than directly implementing each specific article within the UN Declaration.
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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 | 0.112 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.031 | 0.032 |
| Scholarly communication | 0.034 | 0.012 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.022 | 0.019 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".