Model Framework for Consumer Protection and Crypto-Exchanges Regulation
Bibliographic record
Abstract
Cross-border insolvency of crypto-exchanges, cyber-risks, transnational character of activities with cryptocurrencies, and financial frauds on the Internet are among the key threats for individuals who use Bitcoin as an investment. Moreover, crypto-exchanges impose consumer agreements containing provisions limiting their liability for hacker attacks and other clauses promoting inequality in relations with investors. All the named obstacles highlight the vulnerability of unsophisticated individuals investing in digital assets and have pointed out the necessity to adopt an internationally recognized model of rules for crypto-exchanges, otherwise, it will be impossible to effectively protect the rights of investors engaged in the activities of intermediaries exchanging and keeping decentralized cryptocurrencies. The purpose of the study is to elaborate on the fundamentals for constructing an international legal framework protecting consumers from risks arising from the activities of crypto-exchanges dealing with decentralized cryptocurrencies. Based on the methodology of comparative legal study, this paper examines the judicial practice of various countries and the legislation of jurisdictions popular among crypto-exchanges. The research explores the nature of Bitcoin, describes the types of crypto-exchanges and discusses the main approaches to crypto-exchanges regulation. It argues that an international framework on crypto-exchanges should be based on understanding Bitcoin as a commodity which is situated in the place of crypto-exchange incorporation, licensing of crypto-exchanges, and self-regulation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".