From policy to research to policy: Introduction to special section on child welfare issues in Ontario, Canada
Bibliographic record
Abstract
The adoption and implementation of the UNCRC across the globe has taken many forms in various countries, as some develop legislations, national policies, and amend current provisions to work towards eliminating all forms of child violence by 2030, as outlined by SDG 16. Systemic disparities pertaining to international conflicts, power dynamics, and resource limitations hinder the progress of achieving SDG 16. This frequently restrains reporting standards, organization development, and just aid to maltreated children. In terms of the Canadian landscape, Canada acts as a pathfinder country in the adoption of the UNCRC, as it has implemented numerous studies and initiatives in an attempt to end child maltreatment. However, systemic racism, overrepresentation of marginalized communities, and a lack of support for youth as they age-out of care persists in the Canadian child welfare system. While the Ontario child welfare system shares similar struggles, they have made strides in the fields of child maltreatment reporting and data collection, as well as trends and problem identification through the Ontario Incidence Study of Reported Child Abuse and Neglect. First, we present considerations related to the global mandate for child protection, and the challenges that persist amongst marginalized communities. Subsequently, we focus on Canada and, in particular, the Ontario example: the trends from the Ontario Incidence Study of Reported Child Abuse and Neglect (OIS). This child welfare epidemiological project has highlighted the need for greater intersectional adjustments to best protect children, where the iterative research-policy cycle has most effectively been seen with a formal system for the inclusion of lived experience, as in the case of Indigenous peoples. This introduction to the OIS research papers emphasizes that adaptive models for cost-sharing and collaboration are needed to increase the relevance, efficiency and effectiveness of the child welfare system. Continued evaluation to respond to and challenge policy is a critical function of sustained surveillance systems.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".