Mandatory Reporting Legislation in the United States, Canada, and Australia: A Cross-Jurisdictional Review of Key Features, Differences, and Issues
Bibliographic record
Abstract
Mandatory child abuse reporting laws have developed in particular detail in the United States, Canada, and Australia as a central part of the governments' strategy to detect cases of abuse and neglect at an early stage, protect children, and facilitate the provision of services to children and families. However, the terms of these laws differ in significant ways, both within and between these nations, with the differences tending to broaden or narrow the scope of cases required to be reported and by whom. The purpose of this article is to provide a current and systematic review of mandatory reporting legislation in the 3 countries that have invested most heavily in them to date. A comparison of key elements of these laws is conducted, disclosing significant differences and illuminating the issues facing legislatures and policymaking bodies in countries already having the laws. These findings will also be instructive to those jurisdictions still developing their laws and to those that may, in the future, choose to design a system of mandatory reporting.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.030 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".