Exploring Alternate Explanations for Agency-Level Effects in Placement Decisions Regarding Aboriginal Children
Bibliographic record
Abstract
This chapter summarizes a series of published papers that used data from the Canadian Incidence Study of Reported Child Abuse and Neglect (CIS) to explore the influence of case and organizational characteristics on decisions to place Aboriginal children in out-of-home placements. The premise of the analyses was that these influences were consistent with the framework of the Decision-Making Ecology. In Canada, Aboriginal children are overrepresented at all points of child welfare decision-making: investigation, substantiation, and placement in out-of-home care. Case factors accounting for the overrepresentation of Aboriginal children at all service points in the child welfare system include poverty, poor housing, and substance misuse, and these factors, when coupled with inequitable resources for First Nations children residing on reserves, result in the overrepresentation of Aboriginal children in the Canadian child welfare system. For this study, the authors examine case characteristics and organizational factors in a multilevel context, hypothesizing that children are more likely to be placed out of home in agencies that serve a relatively high proportion of Aboriginal children. According to the statistical models presented, the most important of these factors is whether the provincial government operates the child welfare agency. As with the proportion of Aboriginal children on the caseload, the risk of a child being placed is greater in government-run agencies compared to agencies operated by private funders. Further analysis needs to be conducted to fully understand individual- and organizational-level variables that may influence /decisions regarding placement of Aboriginal children.
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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 | 0.019 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".