Issues of investigation of crimes in the sphere of entrepreneurial activity
Bibliographic record
Abstract
В статье в контексте вопросов защиты прав и законных интересов предпринимателей, уголовной политики Российской Федерации в области обеспечения экономической безопасности рассмотрены некоторые особенности расследования преступлений в сфере предпринимательской деятельности. Поднимается тема гуманизации уголовной ответственности за такие преступления, что связано, в частности, с избранием меры пресечения. На основе анализа статистических данных о расследовании преступлений в сфере предпринимательской деятельности за 2021 год и первый квартал 2022 года предлагаются изменения в уголовное и уголовно-процессуальное законодательство Российской Федерации. The article considers some of the special aspects of investigations in the field of economic security in the context of entrepreneur’s rights and interests protection and the criminal policy of Russian Federation. The author discusses the topic of humanization of criminal responsibility for such crimes, which affects the selection of restrictive measures. Based on analysis of statistical data on the investigation of crimes in the sphere of entrepreneurial activity from 2021 and to the first quarter of 2022, the author proposes amendments to the criminal and criminal procedure legislation of the Russian Federation.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| 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".