Modern Trends in the Development and Regulation of the Cryptocurrency Market in the Global Economy
Bibliographic record
Abstract
The article explores current trends in the development and regulation of the cryptocurrency market in the global economy, considering the transformational processes of the financial system and digitalization. The authors examine the nature and classification of the main types of cryptocurrencies, including bitcoin, Ether, stablecoins, altcoins, and tokens, which serve as means of payment, investment instruments, and components of decentralized finance. The article analyzes the dynamics of the cryptocurrency market, its capitalization, and the number of cryptocurrency owners within the regional structure, using statistical data from leading international organizations and analytical agencies. Particular attention is paid to regulatory models in different regions of the world: the legislative practices of the European Union, approaches in the USA and Canada, the United Kingdom, Switzerland, as well as the experience of developing countries, where cryptocurrencies have become popular as a tool to protect against inflation, safeguard investors from fraud and manipulation, and to build trust, thereby promoting financial inclusion. The Ukrainian context has been analyzed, including the adoption of the Law «On Virtual Assets», as well as the role of cryptocurrencies in financing humanitarian and defense needs during periods of military challenges. Key issues have been identified, including the lack of globally unified rules, money laundering risks, cyber threats, and market volatility. The prospects for the development of the cryptocurrency market are substantiated in terms of combining innovation and security through the harmonization of regulatory standards, the development of international cooperation, and the integration of crypto instruments into the legal financial system. The obtained results contribute to a better understanding of the role of cryptocurrencies in the modern economy and provide a foundation for effective State policy in the field of financial innovations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| 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".