Bibliographic record
Abstract
The lack of a unified theoretical position on the definition of electronic money, insufficient understanding of their technical, economic, and legal nature, the discrepancy in the legal regulation of the circulation of such money in different countries is conditioned by the novelty of the institution of electronic money. In turn, due to the rapid progress, the issue of electronic money is becoming increasingly relevant, attracting the attention of lawyers, economists, and society as a whole. The purpose of this study is to analyse the legal status of electronic money, its advantages and disadvantages, considering the practice of the European Union in this matter. The study used a complex of philosophical and worldview general scientific and special scientific methods. The formal-logical method was used to define the basic concepts and legal categories related to the analysis of the legal status of electronic money in Ukraine. The historical method was used to highlight the process of development and establishment of legal regulation of electronic money in Ukraine. The method of systems analysis allowed to identify and formulate the main conclusions and recommendations for increasing the efficiency of cooperation between Ukraine and the European Union in the field of legal regulation of electronic money. Furthermore, when determining the legal status of electronic money, it is important to consider the legislation of the European Union. The study also analyses the differences in the legal status of electronic money from non-cash, virtual, and digital money.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".