On Improving the Procedure for Transferring Securities to Trust Management: An Analysis of Russian and Foreign Anti-Corruption Legislation
Bibliographic record
Abstract
Introduction: the paper analyzes the regulation of the procedure for transferring to trust management of securities, as well as participation interests, shares in the authorized (pooled) capitals of organizations in accordance with the anti-corruption legislation in the Russian Federation and abroad. Purpose: to resolve the issue of improving the mechanism for transferring financial instruments of a civil servant to trust management. Methods: the methodological framework was formed by the methods of scientific cognition, including such main ones as the methods of consistency, analysis and comparative law. Results: the author identifies the legal gaps in the Russian legislation and suggests using positive foreign experience in the application of the anti-corruption legislation in the legal relations for the transfer of securities to trust management in such countries as the USA, Canada, Albania and Chile, as they have comprehensively developed the legal regulation of the institution of trust management of property of civil servants. Conclusions: the paper contains specific proposals for the implementation of foreign legislation into the current legislation of Russia, namely the establishment of a special state trust (trustee), the consolidation of his legal status, the possibility of prohibiting officials from owning and using their assets in a separate regulatory legal act of the federal level.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".