Differentiating between child protection and family support in the Canadian child welfare system's response to intimate partner violence, corporal punishment, and child neglect
Bibliographic record
Abstract
Rates of reported child maltreatment nearly doubled in Canada over the period 1998-2003, an increase that reflects growing awareness of the harmful effects of an expanding array of parental behaviors, including corporal punishment, lack of supervision, and exposure to intimate partner violence (IPV). Some of these situations may benefit from voluntary family support programs outside of the child welfare system. Analyzing a sample of 11,807 investigations, this paper compares cases where the sole concern is exposure to IPV, or hitting a child, or neglect, or other forms of investigated maltreatment. Situations where exposure to IPV or potentially abusive hitting were the sole reason for investigation presented with fewer risk factors and were less likely to lead to ongoing child welfare interventions compared to other maltreatment investigations. While situations involving alleged neglect presented a higher risk profile and elicited a more intensive child welfare response than did exposure to IPV or hitting, opportunities for alternative services were nevertheless identified. The study also found that visible minority families were overrepresented in cases involving hitting and that Aboriginal families were overrepresented in cases involving neglect. Overall the findings support the development of alternative response programs in Canada.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".