Зарубежный опыт правового регулирования отношений в сфере оборота криптовалюты
Bibliographic record
Abstract
The article discusses the foreign experience of cryptocurrency regulation since the beginning of their widespread use on the example of the United Kingdom, the United States, Canada, Australia, Ukraine, Japan, China and Latin America countries. Most of the countries, such as Venezuela, the United States, Canada, Australia, some EU countries, as well as such financial and technological giants as China and Japan are positive-expectant concerning the status of cryptocurrencies. The neutral position of a number of countries (the European Union led by Germany, Latin American countries) is due to the lack of developed legislation regulating cryptocurrency relations. In Ecuador, Thailand, Vietnam, Iceland and Bangladesh cryptocurrencies are prohibited. Venezuela became the first country in the world to create its national cryptocurrency-Petro. Its value is sunstantiated by reserves of natural resources of the country, and the price equals to a barrel of oil. By Petro, the Venezuelan government expects to overcome the economic crisis caused by the USA sanctions and to attract billions of dollars in investments. Venezuela's national cryptocurrency is built on a blockchain platform. It can be used for payments in the country and exchanged for other cryptocurrencies. In Russia, international experience in the field of regulation of cryptocurrency relationships and determining its status resulted in early adoption of the law on the cryptocurrency and the possible emergence of a national cryptocurrency - cryptolabs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".