The Future of Mandatory Reporting Laws: Developing a Legal and Policy Framework for Determining What Reporting Obligations to Impose on Professionals
Bibliographic record
Abstract
Individuals within a professional-client relationship reveal a range of sensitive information that they might otherwise be reluctant to share with others. Mandatory reporting laws â laws that require professionals to report client information to the state â allow governments to extract this information in order to use it for social goals. These laws are widely used yet poorly understood. There is no apparent rationale for what is reportable and what is not. The existing legal literature largely ignores the interactions of these laws with the common law and other statutes, and particularly their compliance with the Canadian Charter of Rights and Freedoms. The literature also considers individual mandatory reporting laws in isolation. This thesis provides a legal and policy analysis of mandatory reporting laws as a family of related laws in the Canadian context. The legal analysis considers the impact of these laws on the clientâ s interests in autonomy, privacy, and access to services, and on religious and conscience interests of the professional. It concludes that existing mandatory reporting laws infringe Charter rights in several ways. Some of these infringements will be justifiable under section 1 of the Charter as reasonable limitations in a free and democratic society. Others likely require legislative amendments. Overall, however, Charter compliance is not a significant restraint on lawmaking in this area, leaving legislators and policymakers vast possibilities to navigate. The policy analysis complements the legal analysis by setting out a four-component framework for evaluating existing laws and new proposals. The first component focuses on the purpose of the law; the second, on the special ability or opportunity of the professional to detect the reportable occurrence; the third, on the connection between the purpose of the law and the purpose of the profession; and the fourth, on the long-term impacts on the client-professional relationship. Together, the legal and policy analysis should enable legislators and policymakers to improve the coherence and consistency of lawmaking in this area. This thesis demonstrates that mandatory reporting laws are a powerful legal tool, but one that should be employed sparingly and carefully because it comes with real though often intangible harms.
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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 | 0.087 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.019 | 0.058 |
| Scholarly communication | 0.030 | 0.024 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.019 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".