The Initial Coin Offering (ICO) Process: Regulation and Risks
Bibliographic record
Abstract
ICOs are very attractive for investors and issuers. ICOs allow funding raising in exchange for cryptographically secure tokens, which are a means of paying for future projects or services. However, there is insignificant regulation of this process all over the world. Some countries have banned crypto assets; others have allowed the free use of tokens but do not give them official status. In this paper, the authors present an overview of the legal regulation of ICOs in different countries, dividing them into three groups: in the first group are the countries with developed legal norms and rules for conducting ICO, they have the subsequent circulation of tokens on their territory; in the second group are the countries that are most friendly to ICOs; the third group of countries has a wait-and-see attitude. The author connect the insufficient law regulation and risks of ICOs in different countries. The types of ICO risks are divided into three main categories: financial, technical, and analytical. The main ways to reduce these risks, depending on their types, are highlighted in this study. They are connected with the improvement of the legal regulation of the publication of a White Paper, the KYC procedure, and the involvement of escrow agents.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".