Legal Status of Cryptocurrency as Electronic Money
Bibliographic record
Abstract
In different countries, the approach to the legal status of cryptocurrencies is significantly different - some countries (USA, EU, Canada, Israel, Singapore, Japan, etc.) have recognized the expediency of using them and are working to create a legal framework that enhances the legal status of virtual currencies ( as electronic money, as exchange funds, as a specific type of currency, etc.), and other countries (China, the Russian Federation)-reject cryptocurrencies and prohibit their circulation. China banned the circulation of cryptocurrency within its own territory after the government almost lost control over the circulation of funds in the country due to their significant spread. In the Russian Federation, cryptocurrency circulation was prohibited due to the conservatism of the financial system, which is not able to quickly respond to the introduction of innovative processes and ensure their proper regulation. Despite the ban, cryptocurrencies in individual countries and their circulation in the virtual space continue to grow. The legal prohibition on the use of cryptocurrencies does not stop the processes of their use, but only does not allow the states that resort to such a ban to take part in regulating the processes of using cryptocurrencies, since they are removed from the process of their circulation.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".