Intersectionality: A Means for Addressing the Needs of Children with Mental Health Issues who are Engaged with the Family Law and Criminal Justice Systems?
Bibliographic record
Abstract
Huge numbers of children in Canada suffer from mental health issues, yet only a fraction gets needed supports and services. Left untreated, childhood mental illnesses carry serious consequences for children, families, and society as a whole. This public health crisis is significantly more pronounced for children who are engaged with the family law (child welfare) and youth criminal justice systems (“crossover youth”). Crossover youth face multiplicative challenges, including disproportionate rates of mental health issues. In this article, I explore how the failure to provide crossover youth with needed supports and services, and the related dire consequences suffered by these children and society more generally (e.g. deteriorating mental health, repeated engagement in the criminal justice system) is tied to the failure in the family law (child welfare) and youth criminal justice systems to recognize the effects of the intersection of the various challenges and disadvantages (e.g. poverty, racism, instability) experienced by these children. I describe the paradigm of intersectionality, and argue that the adoption of an intersectional approach by the family law (child welfare) and youth criminal justice systems is imperative in order for the legal system to meet its mandate and protect and promote the well-being of these vulnerable children.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.039 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".