Racial disparity in the Ontario child welfare system: Conceptualizing policies and practices that drive involvement for Black families
Bibliographic record
Abstract
Racial disparities in child welfare involvement between Black and White children have been well-documented in the United States, but research in this area is relatively underexplored in Canada. Emerging evidence from Canadian studies indicates that Black families are far more likely to be reported for maltreatment concerns, and that these initial disparities persist as families move deeper into the system. Scholars have begun to identify the factors associated with those disparities in Canada, but there is a need for understanding the larger structural and historical context that shapes the opportunities and constraints for Black families living in Ontario. This analysis will situate child welfare in a nexus of anti-Black policy and structure with respect to immigration restrictions, income disparities, residential segregation, and the functioning of linked institutions such as the mental health, education, and legal systems. The cumulative burden of navigating and contending with these larger systemic forces leave Black families vulnerable to a relatively low threshold for reporting maltreatment concerns and risk of harm to Ontario child welfare agencies. This paper documents the alignment between the circumstances created by anti-Black racism at institutional, provincial, and federal levels and the seemingly race-neutral eligibility criteria embedded within Ontario child welfare, which results in disproportionate reporting of Black families.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".