Ответственность за уклонение от исполнения уголовных наказаний по законодательству некоторых государств Северной и Южной Америки
Bibliographic record
Abstract
статья посвящена компаративистскому анализу положений уголовного законодательства ряда государств Северной и Южной Америки, устанавливающего ответственность за деяния, связанные с уклонением от исполнения уголовных наказаний. Целью исследования являлось установление общих и отличительных черт правовой регламентации уголовной ответственности за уклонение от уголовных наказаний по законодательству России и некоторых государств Северной и Южной Америки. В основу научного познания были положены такие методы, как сравнительно-правовой, формально-юридический, структурного анализа. На основе исследования интересующих преступлений, специфики закрепления юридических признаков их видового объекта, субъективной стороны и субъекта, описанных в источниках уголовного права Аргентины, Бразилии, Канады, Мексики, штатов Алабамы, Вашингтон, Калифорния, Техас, Флорида, автор осуществляет систематизацию подходов к криминализации деяний, связанных с исполнением уголовных наказаний, к дифференциации ответственности за них. В статье осуществлен сопоставительный анализ положений УК РФ и законодательств указанных стран, определены точки соприкосновения и границы водораздела концептуального подхода к криминализации соответствующих деяний. Автор также определяет пути оптимизации положений отечественного уголовного законодательства с учетом положительного опыта отдельных из названных государств. the article is devoted to the comparative analysis of the provisions of the criminal legislation of a number of states of North and South America, establishing responsibility for acts related to evasion of execution of criminal punishments. The purpose of the study was to establish common and distinctive features of the legal regulation of criminal liability for evasion of criminal punishments under the legislation of Russia and some states of North and South America. The scientific knowledge was based on such methods as comparative legal, formal legal, structural analysis. Based on the study of the crimes of interest to him, the specifics of securing the legal features of their species object, subjective side and subject, described in the sources of criminal law of Argentina, Brazil, Canada, Mexico, the states of Alabama, Washington, California, Texas, Florida, the author systematizes approaches to the criminalization of acts related to the execution of criminal punishments, to the differentiation of responsibility for them. The article provides a comparative analysis of the provisions of the Criminal Code of the Russian Federation and the legislation of the aforementioned countries, identifies points of contact and boundaries of the watershed of the conceptual approach to the criminalization of the relevant acts. The author also identifies ways to optimize the provisions of domestic criminal legislation, taking into account the positive experience of some of the aforementioned states.
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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 | 0.003 | 0.010 |
| 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 | 0.012 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.015 |
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".