Counteraction to offenses committed with the use of electronic payment systems: new challenges and problems
Bibliographic record
Abstract
Legal, organizational and technical issues of the current state of crime prevention in the field of electronic payment systems in different countries and in Ukraine are considered. The following methods were used in the article: dialectical, documentary analysis, analytical analysis of documents and observations. Identified and analyzed current trends and risks associated with the use of electronic payment systems by legal entities. Electronic payments have been found to be a progressive and convenient innovation on the one hand, which has greatly accelerated the ability of individuals to engage in day-to-day market relations, and on the other, to be unlawfully encroached upon and systematically improved by criminals. Based on this, emphasis is placed on the urgent need for proper protection of payment systems. It is noted that examples of global counteraction to crimes and various offenses committed in the field of electronic payments are developed countries such as the United States, Great Britain, Canada, Singapore, as well as the European Union, especially France and Germany. As a result of the study, it has been noted that the above countries have all the opportunities to provide Ukraine and its citizens, as well as government officials with the necessary guidelines, technical and legal assistance to create an effective mechanism to combat offenses in the use of electronic payment systems.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".