Ten Answers Every Child Welfare Agency Should Provide
Bibliographic record
Abstract
A university-child welfare agency partnership between the Factor-Inwentash Faculty of Social Work at the University of Toronto and Highland Shores Children’s Aid (Highland Shores), a child welfare agency in Ontario, allowed for the identification and examination of ten questions to which every child welfare organization should know the answers. Using data primarily from the Ontario Child Abuse and Neglect Data System (OCANDS), members of the partnership were able to answer these key questions about the children and families served by Highland Shores and the services provided to children and families. The Ontario child welfare sector has experienced challenges in utilizing existing data sources to inform practice and policy. The results of this partnership illustrate how administrative data can be used to answer relevant, field-driven questions. Ultimately, the answers to these questions are valuable to the broader child welfare sector and can help to enhance agency accountability and improve services provided to vulnerable children and their families.
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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 | 0.022 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.054 | 0.010 |
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".