Child Welfare Workers’ Biases in Decision-Making: The Potential for Influence on Disparity and Disproportionality
Bibliographic record
Abstract
There is a public outcry to address systemic racism at all levels of government. Due to its colonial roots, and origins in White, middle-class values, Ontario’s child welfare system has allowed for the differential treatment and overrepresentation of vulnerable populations, specifically Indigenous Peoples, racialized/ethnic groups and families who live with poverty. One hypothesis for the ongoing disparity and disproportionality of vulnerable groups in child welfare is the impact of implicit thinking, initial judgements and reactions in decision-making by child welfare workers. This presentation will provide an overview of the history of child welfare in Ontario, and current legislation and structure. Through a framework of intersectionality, anti-racism, and anti-privilege, guided by Signal and Dual Process theory, the results of a rapid systematic review of studies that assessed for biases in child welfare worker decision-making will be discussed. The gaps in knowledge, specifically the paucity of Canadian child welfare research and the lack of understanding of the influences of biases in decision-making will be explored. This research identifies a critical need for research that attempts to understand the influence of implicit and/or explicit biases in child welfare high-stakes decisions. Without further understanding, attempts to implement anti-black racism, anti-racist and social justice frameworks to address disparity and disproportionality in child welfare may be ineffective as child welfare worker biases may continue to impact areas of judgement and decision-making.
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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 | 0.086 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".