The absence of language: A critical race discourse analysis of Ontario’s child welfare legislation and the impacts on Black families
Bibliographic record
Abstract
Background: The research pertaining to racial disparities for Black families in child welfare is relatively limited in Canada. Recent research reveals that the overrepresentation of Black families in Canadian child welfare systems typically begins at the reporting or investigation stage and continues throughout the child welfare service and decision-making continuum. This research is occurring against the backdrop of increasing public acknowledgement of Canada’s historic anti-Black policy-making and institutional relationships to Black communities. Though there is increased awareness about anti-Black racism, there has been limited exploration of the connection between anti-Black racism in child welfare legislation and how this policy generates disparities for Black families in both child welfare involvement and outcomes – this paper seeks to fill this gap in knowledge. Objective: The objective of this paper is to explore the entrenchment of anti-Black racism within the child welfare system by critically assessing the language and absence of language within the guiding legislative and implementation policies. Methods: Utilizing a critical race discourse analysis method, this study explores the entrenchment of anti-Black racism within the Ontario child welfare system by critically assessing the language and absence of language within the guiding legislative policies that shape practice for Black children, youth, and families. Results: The findings revealed that though the legislation does not explicitly address anti-Black racism, there were instances where the legislation indicated that race and culture may be considered in responding to children and families. The lack of specificity, particularly in the Duty to Report, has the potential to contribute to disparate reporting and decision-making for Black families. Conclusions: Policy makers should acknowledge the history of anti-Black racism that informed the development of the legislation in Ontario and move towards tackling systemic injustices that disproportionately affect Black families. More explicit language will shape future policies and practices to ensure that the impact of anti-Black racism is considered across the child welfare continuum.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.041 | 0.033 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".