Judicial Jurisdiction of Consideration of Cases on Administrative Offences in the Field of Finance and Monitoring of their Implementation
Bibliographic record
Abstract
Taking into consideration the rapid growth of administrative and criminal offenses in financial activities, criminal attempts to affect negatively development of the state in the economy and finance segment, negative trends in the detection and investigation of financial offenses indicate their low level and the balance of non-investigation, which have increased significantly compared to previous years. The purpose of the study is to analyze and summarize the administrative and jurisdictional powers of courts (judges) related to the monitoring and control of proceedings in cases of administrative offences in financial sphere. During the work, the main research methods were analysis, synthesis, comparison and in-depth research of normative legal acts of Ukraine related to this topic. The result of the study is a full-fledged analysis of the legislative, regulatory and legal framework of Ukraine regarding judicial jurisdiction over cases of administrative offenses in the financial sphere, and monitoring their implementation. Thanks to the study of the literature on this topic, the peculiarities of handling cases of administrative offenses, the interaction of law enforcement agencies were discovered, and the conclusion was made that the implementation of the principle of the rule of law and legality to protect the rights, freedoms and legitimate interests of the individual, society and the state was a real method of influencing the sphere of finance.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".