An Analysis of Child and Youth Participation in Practice: Lessons Learned from Child and Youth Advocate Offices and the Aboriginal Youth Court
Bibliographic record
Abstract
Abstract In 2020, UNICEF Canada released Report Card 16 revealing that Canada ranks in the bottom tier compared to other wealthy countries in terms of child and youth well-being. The Report Card highlights that promoting participation is required to improve this ranking. Recognising the connection between child well-being and participation, this chapter explores youth-serving institutions in Canada to understand how participation materialises in these settings. Through interviews with provincial and territorial Canadian child and youth advocates, this chapter first explores advocate offices that serve young people facing challenges. These are the only group of child and youth advocates in Canada that have formal legal mandates to implement children's rights at the provincial and territorial level. Comparatively, through interviews with justice-involved youth we analyse the youth justice system. By adjusting the setup of the court space and attempting to minimise power imbalances, we discuss how Canada's first and only Aboriginal Youth Court (AYC), promotes participation and engagement. Through a comparative case analysis, this chapter explores where barriers exist in terms of conceptualising and implementing participation rights, and where opportunities and best practices may be leveraged across child and youth serving institutions in Canada.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".