Financial and Legal Regulation of Electronic Money Circulation in Developed Countries
Bibliographic record
Abstract
The relevance of this paper is conditioned by the study of the regulation of electronic money circulation in the countries of the world, in particular in Ukraine, the specifics of carrying out activities in the field of issuing electronic money and establishing the regulatory basis for such activities. The purpose of this study is to investigate and analyse the financial and legal regulation of electronic money circulation, and to establish the specifics of issuing electronic money. The following methods were used: dialectical method, formalisation, hermeneutical, legal, formal and legal, logical and legal methods, system, structural and functional method, axiomatic method, induction and deduction, analysis, synthesis, etc. The findings are the definition and establishment of the features of the concept of "electronic money" and "electronic money circulation"; the establishment of the regulatory framework for regulating electronic money; investigation of the history of the establishment of electronic money in the world; the regulations of the sphere of electronic money circulation in Ukraine are studied and ways to improve this sphere are proposed. Subjects who can engage in activities aimed at issuing electronic money were identified and the features of their activities were determined. The provisions set out in this paper are of practical value primarily for subjects whose activities are aimed at implementing electronic money circulation and regulating this activity, and for persons who somehow participate in electronic money circulation and whose rights may be violated by the providers of such services.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 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".