Putting Our Minds Together: Aspirations and Implementation of Bill C92, An Act Respecting First Nations, Inuit and Métis Children, Youth and Families in Canada
Bibliographic record
Abstract
In 2020, Bill C92, or an Act Respecting First Nations, Inuit and Metis Children, Youth and Families, came into force in Canada. The Act historically recognized and affirmed Indigenous jurisdiction over child and family services and established national minimal standards for service delivery. In 2024, the Supreme Court of Canada upheld the constitutionality of the Act in an appeal from a Quebec Court of Appeal reference case. The Court stressed all parts of the Act must be viewed as “integrated parts of a unified whole” and required the braiding together of Indigenous laws, state laws and international laws into a “single strong rope.” The Act’s aspirations remain in tension with ongoing challenges in implementation. This article outlines the main provisions of the Act. It then examines the law-making efforts and accomplishments of Indigenous governments exercising jurisdiction using the Act, along with some of the hopes and obstacles encountered through this work. Next, it considers some of the emerging jurisprudence interpreting the Act, and some of the implications this case law has on whether the stated purposes of the Act are being achieved. It concludes by highlighting the ongoing uncertainty and hopes for realizing the full potential and aspirations of the Act.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.049 | 0.033 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| 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".