Bibliographic record
Abstract
Winds of change are blowing over the legal profession. Yet, compared to other professions and industries, legal services regulation remains very much a laggard. For the most part, legal services regulation remains rigid, reactive and complaint-based. These are not characteristics that are considered regulatory best practices. Recognizing this, a number of law societies are contemplating more proactive, compliance-based regulation. Indeed, some Canadian legal regulators have already turned those thoughts into action, most notably the Nova Scotia Barristers’ Society and the Law Society of Alberta. We assert that Canadian legal regulators should continue down this path and move to risk regulation, a more focused and efficient system of regulation. This is a positive development and should be embraced by all Canadian law societies. We assert that the only legitimate normative basis for regulation of the legal profession – whether that continues to be self-regulation or some other form of regulation as exists in other jurisdictions – is the protection of the public interest. This should not be a particularly controversial proposition; it is part of the standard justification for self-regulation of the legal profession. However, much of the criticism of self-regulation relates to the failure of the legal profession to live up to this standard, or the profession’s pursuit of its own interests. We believe that risk regulation provides a better, more targeted way for Law Societies to fulfill their mandates to regulate legal services in the public interest. Our paper has four parts including this introduction. Part II sets out the normative case for risk regulation. We begin by explaining the four different roles that risk plays in regulation: as an object of regulation; as a justification for regulation; as an organizing principle for operations; and as a measure of accountability. This part addresses the first two aspects of risk regulation and identifies deficiencies in current approaches to legal regulation in Canada. We then explain why risk regulation would be an improvement in legal regulation. In Part III we address the third aspect of risk regulation, using practical examples to illustrate how risk regulation is actually done. In doing so, we draw on examples from the regulation of lawyers in other jurisdictions, specifically England and Wales and Australia. We also draw upon examples from the regulation of medicine and finance in Canada. In Part IV, our paper ends with a brief conclusion in which we recognize that significant cultural and operational changes are required to move to a risk regulation regime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.045 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".