Возможности применения зарубежного опыта для совершенствования антикоррупционного законодательства Российской Федерации в системе государственной службы
Bibliographic record
Abstract
Как известно, Россия продолжает оставаться страной с высоким уровнем коррупции, в отличие от ряда стран Северной Европы и Канады. Данная статья — это попытка изучения опыта противодействия коррупции в системе государственной службы указанных стран. Методы противодействия взяточничеству в названных странах складывались десятилетиями, приносят положительные результаты и вполне могут быть восприняты российским государством и обществом. Автор отмечает, что борьба с коррупцией в системе государственной службы должна носить системный характер, как и совершенствование законодательства в этой сфере. Необходимы оптимизация антикоррупционных методов борьбы с данным негативным явлениям, воспитание неприятия коррупции в обществе с раннего возраста, развитие реальных демократических институтов, широкое и «без купюр» освещение в СМИ проявлений коррупции на госслужбе как на федеральном, так и на региональном уровне. As you know, Russia continues to be a country with a high level of corruption, unlike a number of countries in Northern Europe and Canada. This article is an attempt to study the experience of combating corruption in the public service system of these countries. Methods for combating bribery in these countries have evolved over decades, bring positive results and may well be accepted by the Russian state and society. The author notes that the fight against corruption in the civil service should be systemic, as well as the improvement of legislation in this area. It is necessary to optimize anti-corruption methods of combating these negative phenomena, to nurture the rejection of corruption in society from an early age, to develop real democratic institutions, and to widely and “uncut” media coverage of manifestations of corruption in the civil service both at the federal and regional levels.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.016 |
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".