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Record W4380481992 · doi:10.6000/1929-4409.2020.09.241

Evasion Restriction of Customs Payments in the Field of Customs Affairs

2022· article· en· W4380481992 on OpenAlexvenueno aff
Khayrullina Rezeda Gazinurovna, Shakirova Albina Abdulkhakovna, Aglyamova Gulnaz Makhiyanovna, Khamitov Radik Nakimovich, Kharisova Elvira Anvarovna

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Policy Issues
Canadian institutionsnot available
FundersKazan Federal University
KeywordsPaymentEvasion (ethics)LegislationCriminal liabilityLiabilityLegislatureBusinessCriminal lawLawTax evasionTerrorismEconomicsLaw and economicsInternational tradeAccountingPolitical scienceFinancePublic economics

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.147

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.062
GPT teacher head0.399
Teacher spread0.337 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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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