Self-regulating organizations of professional participants of the securities market abroad and in Russia: role, types, features of activity
Bibliographic record
Abstract
В статье рассмотрено понятие «саморегулируемая организация», определена нормативно-правовая база. Также рассмотрены существующие в настоящее время модели осуществления саморегулирования. Кроме того, проведен сравнительный анализ саморегулируемых организаций в зарубежных странах, таких как США, Япония, Канада. На основе анализа выявлены основные недостатки, предложены меры по их устранению. The article considers the concept of "self-regulating organization" and defines the regulatory framework, as well as the currently existing models of self-regulation. In addition, a comparative analysis of self-regulatory organizations in foreign countries, such as the USA, Japan, Canada, was carried out. Based on the analysis, the main shortcomings are identified, measures to eliminate them are proposed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".