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.A number of Canadian provinces, for example, have provisions that are tantamount to regulatory objectives.Australia is also in the process of developing such objectives and routinely uses "purpose statements" when enacting legal profession regulation.However, many countries-including the United States-have not explicitly identified regulatory objectives and do not use purpose statements.This Article analyzes various regulatory objectives that have been adopted or proposed.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.
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 | 0.055 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".