The contributions of family poverty indicators to professionals' decision-making in child physical punishment cases : an analysis of the Canadian Incidence Study of Reported Child Abuse and Neglect
Bibliographic record
Abstract
This study examined the predictive influence of family poverty on professionals' decision-making in cases of child physical punishment reported to child welfare agencies. Data from the Canadian Incidence Study of Reported Child Abuse and Neglect wereused.An examination was made of the influence of five indicators of poverty on six investigation outcomes.The poverty indicators were family size, household education, whether the family lived in public housing, whether the family's main income source was social assistance, and whether the family lived in unsafe housing conditions.In addition, a Poverty Indicators Index was constructed from these five variables to assess whether the family's "total picture" influenced investigation outcomes.The outcome variables examined in this study were case substantiation, ongoing child welfare services, child and family referrals, out of home placement, applications to child welfare court and police involvement.The predictive power of the poverty indicators and the Poverty Indicators Index \ /as assessed through logistic regression analyses.Together, the predictor variables did not account for more than 6Yo of the explained variance of any of the outcome variables.Unsafe housing was the only poverty indicator found to be a significant predictor of all the outcome variables, with the exception of police involvement.In addition, the magnitude of the Poverty Indicators Index did not affect the likelihood of any of the investigation outcomes.These f,rndings suggest that poverty indicators do not influence professional decision making in cases of physicalpunishment reported to child welfare agencies in Canada.The findings have implications for the ongoing development of policy aimed at reducing parental use of physical punishment.physical punishment Cases and SESTable of Contents Abstuact ...
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".