Regulation of advocacy profession: global trends
Bibliographic record
Abstract
The objective of the article is to analyze the regulation of the legal profession and its global trends. There are many different types of regulators globally, and many different sources and methods of regulation. There is no simple approach to setting goals for regulating the legal profession in different legal systems. Although self-regulation of the legal profession is considered the basis for adhering to the standard of its independence, at the same time, academics recognize the existence of the theory of the management of the legal profession. To study these problems, the authors conducted a comparative study of the regulatory models of the legal profession in the world in terms of compliance with international standards of legal independence in different legal jurisdictions and made some suggestions to improve the legal regulation of the legal profession in Ukraine. Empirical sources for scientific research were international documents, court decisions, national legislation of Great Britain, Canada, the United States, Ireland, Scotland, Australia and others, and the work of scientists. The article uses general scientific methods - dialectic, analysis, synthesis, analogy, etc., and special methods, particularly legal, historical, and formal comparative law.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".