Comparative Perspectives on Lawyer Regulation: An Agenda for Reform in the United States and Canada
Bibliographic record
Abstract
Regulation shapes every area of modern economic life. Getting regulation right-knowing when it is necessary, what it should accomplish,and what form it should take-is a critical part of policymaking in every society.Developing an effective oversight structure requires a complex analysis of each society's particular historical, cultural, and legal foundations.Regulation of the practice of law is no different, although it has received surprisingly little public attention in the United States and Canada.That is not for lack of problems, and other countries with similar legal systems, such as Australia and England and Wales, have begun to do better at addressing common oversight failures.This Article explores why problems in American and Canadian legal regulation persist, and identifies reform strategies that build on recent innovations from abroad.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.033 | 0.017 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".