Policy Makers’ Perspectives on the Utility of a National Study of Child Maltreatment
Bibliographic record
Abstract
Reliable national child maltreatment data are needed for developing and modifying policies aimed at preventing child maltreatment and helping child victims of maltreatment. Health Canada hosted a daylong forum in 2002 to solicit feedback from senior Government of Canada policy and program officials involved in child welfare programs and research in regard to the data collected in the Canadian Incidence Study of Reported Child Abuse and Neglect (CIS). This article reviews the discussions and debates regarding the utility of the CIS data for government policy makers and reflects on the implications for surveillance and knowledge in the area of child maltreatment. The key themes are definitions and measurement issues, the value of enhanced and additional data, and challenges to linking research and practice.
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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 | 0.317 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.021 | 0.058 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.021 | 0.031 |
| Insufficient payload (model declined to judge) | 0.003 | 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".