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Issues of investigation of crimes in the sphere of entrepreneurial activity

2022· article· ru· W4320067126 on OpenAlexaboutno aff
А.И. Бастрыкин

Bibliographic record

VenueВестник Московской академии Следственного комитета Российской Федерации · 2022
Typearticle
Languageru
FieldSocial Sciences
TopicLegal and Policy Issues
Canadian institutionsnot available
Fundersnot available
KeywordsRussian federationLegislationContext (archaeology)Quarter (Canadian coin)Political scienceCriminal responsibilityField (mathematics)CriminologyLawCriminal lawSociologyBusinessEconomic policyHistory

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.004
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.050
GPT teacher head0.347
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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