Evasion Restriction of Customs Payments in the Field of Customs Affairs
Bibliographic record
Abstract
The main aim of the study is to analyze the basic problem of evading customs payments, which entails serious financial losses for the state budget. The article seeks to discuss the problematic issues of criminal liability for evading payment of customs duties levied on an organization or an individual, as a type of customs crime. Ways of legislative improvement of criminal law norms and aimed at combating crimes in the field of customs regulation is particular, with evasion of customs duties levied on organizations or individuals at the present stage, are proposed. The article provides an analysis of the criminal law on evasion of customs payments and smuggling, and shows a method for distinguishing related offenses using the example of evasion of customs payments and smuggling. The authors give for us a comparative analysis of the criminal law of the Russian Federation, providing for liability for smuggling and evasion of customs payments, and give the criminal legal qualification of these illegal acts. The characteristics of the elements of smuggling and tax evasion fees charged to the organization or individual are given. Based on the study, the authors draw conclusions about the development and improvement of legislation on tax evasion.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".