Free, Prior, and Informed Consent and Reconciliation in Canada: Proposals to Implement Articles 19 and 32 of the UN Declaration on the Rights of Indigenous Peoples
Bibliographic record
Abstract
Canadian Prime Minister Justin Trudeau has repeatedly promised to meet the Indian Residential School Truth and Reconciliation Commissionâs recommendation to implement the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) as a framework for reconciliation. This commitment is significant as Canadaâs position on UNDRIP has been highly contested. In particular, the compatibility of UNDRIPâs Free, Prior, and Informed Consent (FPIC) standard with Canadian law has been repeatedly called into question. This work evaluates the possibility and importance of implementing FPIC in Canada. It begins with an overview of FPIC internationally and of FPIC in relation to Canadian law. It then suggests potential policy measures to implement two key articles of UNDRIP containing FPIC requirements. To meet Article 32âs FPIC requirement for project approvals for development on their territory, this work draws upon Assembly of First Nations recommendations in suggesting amendments to environmental assessment processes. To implement Article 19âs FPIC requirement for legislative and administrative measures affecting Indigenous Peoples, it suggests revisiting the Royal Commission on Aboriginal Peopleâs recommendation for a House of First Peoples (or Aboriginal Parliament) and informs this suggestion with reflection on the Sámi peoplesâ experience with the Sámi Parliament in Norway.
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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 | 0.139 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.029 | 0.023 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.022 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".