Foreign Experience in the Legal Regulation of Utility Token Circulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study offers a comparative legal analysis of the statutory recognition and private-law regime of utilitarian digital rights (utility tokens) in foreign jurisdictions and the Russian Federation. It shows that this institution emerged at the intersection of contract and financial law and is evolving from a technologically neutral, func-tional approach toward more formalized constructs. Drawing on the experience of the United States, Singa-pore, the United Kingdom, Japan, the UAE, Switzerland, South Korea, Germany, Australia, and Canada, the paper identifies key criteria for distinguishing utility tokens from investment instruments (including via the Howey test) and examines the regulatory consequences of such classification. Particular attention is paid to the Russian model of special regulation: the normative definition of utility digital rights in the Civil Code of the Rus-sian Federation, their issuance and circulation via the framework of investment platforms, and the correlation with digital financial assets. The paper explores the practical effects of different models (the regulatory exclu-sion model, the specialized regulation model, and the general contract law model) for market participants – risk allocation, consumer and investor protection, compliance requirements, and legal certainty while preserving flexibility for innovation. It analyzes the hybrid nature of tokens and approaches to NFTs, substantiating the need for combined regulatory regimes where mixed characteristics are present. Proposals are formulated to optimize national regulation, including clarification of qualification criteria, a risk-oriented typology, proportion-ate requirements for issuers and platforms, as well as mechanisms for law enforcement coordination to sup-port the sustainable development of digital civil turnover.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it