A critical analysis of the overrepresentation of First Nations children and families in the Ontario child welfare system and disparities in providing ongoing child welfare services
Bibliographic record
Abstract
First Nations children are chronically overrepresented in the child welfare system in Canada. This is largely a result of the effect that colonization has on Aboriginal peoples, but also evidence of colonialism being reproduced through current discriminatory legislation and practices. This three-paper dissertation employed a secondary analysis of data to examine the extent of the overrepresentation of First Nations children involved with child welfare in Ontario. Moreover, this study critically examines investigations of reported maltreatment to understand what is driving the overrepresentation of First Nations children. The results show that overrepresentation is a predictable outcome in a system predicated on assimilative objectives. In Ontario, First Nations children represent 2.5% of the child population; they represent 7.4% of child maltreatment-related investigations. For every 1,000 First Nations children in Ontario, 160.3 were involved in investigations compared to 54.4 per 1,000 White children. Overrepresentation was most pronounced for investigations of neglect. Rates of substantiation (3.4 times), ongoing services (4.2 times), child welfare court (5.7 times), and child welfare placement (7.5 times) were higher for the First Nations child population and disparities increased as children moved further into the child welfare system. Caregiver concerns were the main drivers of transfers to ongoing services for both First Nations and White children. For investigations involving White children, after controlling for caregiver concerns, workers were more likely to transfer a case for ongoing services when child psychological harm was present. While the proportion of children identified with psychological harm was similar across both groups, workers placed more weight on a White child experiencing psychological harm. The notion that workers might have different standards for decision-making for First Nations children compared to White children is concerning. Overall, the findings indicate that structural risks have not been addressed, putting First Nations families at risk for child welfare involvement. Structural issues such as chronic poverty and systemic racism are indicative of the legacy of the residential school system and produce the conditions that result in children coming to the attention of child protection services. Overrepresentation will continue unabated if the immense social inequities for First Nations children are not addressed.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 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) | 0.003 | 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".