Legislation’s Influence on Judiciarization: Examining the Effects of Statutory Structure and Language on Rates of Court Use in Child Welfare Contexts
Bibliographic record
Abstract
This paper investigates the extent to which legislation influences decisions of child welfare workers regarding the referral of cases to court. It studies three Canadian jurisdictions: Quebec, Ontario, and Alberta, each of which takes a different legislative approach to the issue of court involvement in child protection. A critical examination of child welfare statutes in these provinces led to the prediction that rates of court use – or ‘judiciarization’ – would be highest in Quebec, followed by Ontario, and then Alberta. These predictions were then compared with data reflecting actual judiciarization rates in these three provinces for the year 2006. This data contradicted our initial predictions, in that Ontario’s rate of court use for child welfare cases was the highest of the three provinces, followed by Alberta, and then Quebec. Our research results thus suggest that legislation alone does not drive judiciarization in the child welfare context. As such, this paper illuminates the need for further study of the way in which child protection workers understand legislation as influencing their professional responsibilities and choices. Moreover, it indicates that further consideration is needed into how the use of judicial versus extra-judicial institutions might affect child welfare outcomes.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".