Adopting Regulatory Objectives for the Legal Profession
Bibliographic record
Abstract
In 2007, the United Kingdom adopted a new law called the Legal Services Act. This Act radically changed certain aspects of U.K. lawyer regulation. Section 1 of that Act identified eight “regulatory objectives” that provide the basis for the regulation of the legal profession. The United Kingdom is not the only jurisdiction that has identified regulatory objectives. Most Canadian provinces, for example, have provisions that are tantamount to regulatory objectives. Australia routinely used “purpose statements” when enacting legal profession regulation and was in the process of developing regulatory objectives at the time this article was written. Despite these examples, however, many countries—including the United States—have not explicitly identified regulatory objectives and do not use purpose statements.\nThis Article analyzed both adopted and proposed regulatory objectives. It places the use of regulatory objectives and purpose statements in lawyer regulation in a broader context by describing some of the recent profession-specific and non-profession-specific regulatory reform initiatives. The Article recommends that jurisdictions that have not yet adopted regulatory objectives for the legal profession do so. Finally, the Article concludes by offering recommended regulatory objective concepts for jurisdictions to consider. The Article’s Appendices set forth the regulatory objectives found in the 2007 UK Legal Services Act, in Canada, Australia, and other countries. This article was written before the ABA’s 2016 adoption of ABA Model Regulatory Objectives for the Provision of Legal Services in Resolution 105: https://perma.cc/3WQG-LDBW. For a document that includes the ABA Regulatory Objectives, as well as regulatory objectives from Nova Scotia, and those adopted by the Supreme Courts in Colorado, Illinois, and Washington, see Laurel S. Terry, Examples of Regulatory Objectives for the Legal Profession (2019), https://works.bepress.com/laurel_terry/89/.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".