Disproportionality and Disparity of Black Children in the Child Welfare System of Ontario, Canada
Bibliographic record
Abstract
While the disproportionate and disparate representation of Black children in the child welfare system has been the subject of over forty years of research in the United States, such research is now emerging in Canada. This three-paper dissertation examines disproportionality and disparity of Black children in the child welfare system of Ontario, Canada. The first paper uses data from the first five cycles of the Ontario Incidence Study of Reported Child Abuse and Neglect (OIS) to compare incidence data on Black and White families investigated by Ontario’s child welfare system over a twenty (20) year period. The results show that the incidence of investigations involving White families almost doubled between 1998 and 2003. For Black families, the incidence increased almost fourfold during the same period. The second paper uses the same OIS data to examine the impact of decision-making tools on Black families and how they might have contributed to their overrepresentation in the child welfare system. The paper seeks to explore potential drivers of the increase and their impact on Black families. This paper suggests that reports of physical abuse and exposure to intimate partner violence are among the many explanations for the overrepresentation of Black children in Ontario’s child welfare system. The third paper uses focus groups to explore the findings from the first two papers in order to interpret them through the perspectives of community service providers and child welfare workers. This paper generates a number of themes around racism and bias; lack of cultural sensitivity; lack of workforce diversity/training; lack of culturally appropriate resources; assessment tools; duty to report; fear of liability; lack of collaboration between child welfare workers and Black families; and poverty. This dissertation concludes with a summary of key findings, limitations and implications for theory, policy and practice.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".