The Main and Intersecting Predictive Effects of Out-of-Home Child Welfare Placement, Sex, and Ethnicity on Arrest in Canada
Bibliographic record
Abstract
Abstract In Canada, Indigenous Peoples are disproportionately represented in both the child welfare and criminal legal systems. Despite longstanding recognition of this inequity, limited research has examined the impact of out-of-home child welfare placement on arrest within the Canadian context, particularly for Indigenous populations. This study investigates the relationship between intrusive child welfare involvement and arrest, with a focus on the moderating roles of sex and ethnicity. Using data from Canada’s General Social Survey, four hypotheses were tested. Controlling for all study variables, Indigenous Peoples were approximately four and a half times as likely as White individuals to have been arrested in the past 12 months. Individuals with a history of out-of-home placement were more than twice as likely to have been arrested compared to those without such experience. Indigenous Peoples who had experienced out-of-home placement were nearly six times as likely as their White counterparts to have been arrested. The most extreme disparity was observed among Indigenous females with out-of-home placement histories, who were over 94 times more likely to have been arrested than White females with no such history. Even without placement histories, Indigenous females faced the highest risk of arrest. These findings underscore the profound and compounding risks associated with the removal of Indigenous children, particularly girls, from their homes, highlighting the urgent need for policy and systemic reform.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".