An Assessment of Canada's UNDRIP Act and jurisprudence on the duty to consult in relation to the United Nations Declaration on the Rights of Indigenous Peoples
Bibliographic record
Abstract
The United Nations Human Rights Council adopted the Declaration on the Rights of Indigenous Peoples in 2006 due to decades-long international efforts to create a comprehensive instrument safeguarding Indigenous rights. In protecting human rights, under international law, States are obliged to perform their obligations to respect, protect, and fulfil. This paper examined Canada's adherence to the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) and how it fares compared to other Countries that have also fully supported the Declaration. The primary aim of this research is to delineate Canada's present actions concerning its obligations and commitments to the UNDRIP. This paper utilized a descriptive design to analyze Canada's actions toward reconciliation and recognition of Indigenous rights. The paper employed a systemic analysis of related literature. In 2021, the UNDRIP Act came into force. A notable concern about this Act is its limited and negligible effect on the community. The UNDRIP Act may not have fulfilled its mandate, and its impact is inadequate to fulfill its purpose. The creation of a national legal framework capable of delivering a holistic implementation of the UNDRIP is necessary, and this prevents the piecemeal and band-aid solutions to the current woes of Canadian Indigenous peoples.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.027 | 0.017 |
| Scholarly communication | 0.014 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".