The Power of Lawyer Regulators to Increase Client & Public Protection Through Adoption of a Proactive Regulation System
Bibliographic record
Abstract
The idea behind this Article is Ben Franklin's statement that "an ounce of prevention is worth a pound of cure." This Article builds on the author's prior articles that argue that one can think about lawyer regulation issues as involving who-what-when-where-why-and-how to regulate issues. This Article addresses the issue of "WHEN" regulation should occur. It argues that regulators should be trying to PREVENT problems, as well as responding AFTER problems occur. This Article is primarily directed toward those who regulate U.S. lawyers. The Article argues that the lawyers who head regulatory bodies in the United States have the ability to adjust the focus of the regulator for which they work in a way that will increase client and public protection. The Article further argues that it is appropriate for lawyers in these positions to exercise this power and that they should do so. The Article concludes by offering two concrete recommendations. The first recommendation is that those who are in charge should, upon reflection, adopt a mindset in which they recognize that the regulator should be systematically trying to prevent problematic behavior by lawyers, as well as responding to such behavior after it occurs. The second recommendation is that regulators should take advantage of a tool they already have at their disposal, which is their state’s equivalent to ABA Model Rule of Professional Conduct 5.1. If jurisdictions added two questions about Rule 5.1 to lawyers’ annual bar dues statement, along with a link to additional online resources, they would be able to emulate actions that have been taken in Australia and Canada. The data suggest that such steps could dramatically reduce client complaints, lead to improved client service, and change the ways in which lawyers operate their law practices. This article addresses the topic of proactive lawyer regulation or PMBR, which is the subject of the American Bar Association's Resolution which will be voted on during the August 2019 ABA Annual Meeting. See Resolution 107, https://perma.cc/A97T-QV23. For a 3-page blog post addressing similar issues, see Laurel Terry, When it Comes to Lawyers… Is an Ounce of Prevention Worth a Pound of Cure?, JOTWELL (July 13, 2016), https://works.bepress.com/laurel_terry/92/.
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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 | 0.068 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".