Examining Child Welfare Decisions and Services for Asian-Canadian Versus White-Canadian Children and Families in the Child Welfare System
Bibliographic record
Abstract
Using administrative child welfare data from the Ontario Child Abuse and Neglect Data System (OCANDS), this study compared the profiles of Asian-Canadian and White-Canadian children and families that experienced a case closure after an investigation instead of being transferred to ongoing child protection services (CPS). Child protection investigations involving Asian-Canadian and White-Canadian children and families that were transferred to ongoing CPS presented a different profile of case characteristics and caregiver and child clinical needs. Asian-Canadian children and families received ongoing CPS for over a month longer than White-Canadian children and families and were less likely (odds ratio [ OR] = 0.39) to be reinvestigated for any form of maltreatment-related concerns within 1 year after case closure. It appears that child protection investigations involving Asian-Canadian children and families are less likely to be closed prematurely than White-Canadian children and families, and the child protection system may be meeting the needs of Asian-Canadian communities. Alternatively, it is possible there is unaccounted biases that may be reflective of systemic problem of discriminative practices in the child protection system. Further research is needed to explore this phenomenon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 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".