Commentary on Canadian Child Maltreatment Data
Bibliographic record
Abstract
The issue of how to best collect child maltreatment data is a key concern within the Public Health Agency of Canada (PHAC). We argue that maltreatment data can be collected from children, adolescents, and parents with approaches that are accurate, methodologically robust, legal, and ethical. It has been done in other countries. First, we clarify ongoing child maltreatment data collection by the Canadian government and address PHAC initiatives to include child maltreatment questions in national contemporaneous surveys. Second, we identify examples of population-based studies with child, adolescent, and parent respondents. Third, we highlight some measurement considerations. Fourth, we address ethical considerations in conducting this type of research.
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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 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.055 | 0.052 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".