Realising Indigenous Children’s Rights: Aligning the Principles of Self-Determination and the Best Interests of the Child. The Case of Indigenous Peoples in Canada
Bibliographic record
Abstract
he historical legacy of colonialism worldwide has created challenges for Indigenous peoples, including racial discrimination and marginalization. This is reflected in, inter alia, the overrepresentation of Indigenous children in child welfare systems, even in countries performing comparably high in human right advancements otherwise. While there may be a broad spectrum of reasons for this issue, this thesis focuses on the problematic dissonance of two human rights principles in their application, the best interests of the child (BIoC) and Indigenous peoples’ right to self-determination. The thesis argues that the BIoC principle has limited Indigenous peoples right to self-determination, because the application and decontextualized interpretation of the BIoC might not reflect the Indigenous communities’ understanding and values. Consequently, the limitations of the right to self-determination have resulted in the BIoC not being applied in a way that truly reflects the Indigenous child’s best interests and needs in their socio-cultural context. The thesis examines how these two principles can be aligned, so that they not only coexist but further enhance each other.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.025 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".