Crimes Related to Cryptocurrencies in the Iranian Legal System and the Common Law System
Bibliographic record
Abstract
Cryptocurrencies, as an emerging phenomenon in the world of finance and technology, have attracted significant attention. These digital currencies operate based on blockchain technology and facilitate financial transactions without the need for traditional intermediaries such as banks. In the Iranian legal system, due to the novelty of the topic, there is no specific and comprehensive legislation to address crimes related to this domain. Nevertheless, some existing laws on combating money laundering and financial crimes can be extended to partially cover cryptocurrencies. The Central Bank of Iran and other financial institutions are currently in the process of formulating regulations to manage and supervise this field. On the other hand, in the common law system, which is implemented in countries such as the United States, Canada, and the United Kingdom, multiple laws have been enacted to confront crimes associated with cryptocurrencies. Institutions such as the Securities and Exchange Commission and the Financial Crimes Enforcement Network under the U.S. Department of the Treasury have developed detailed regulations to monitor and control cryptocurrency transactions. This article examines the differences and similarities in the laws and regulations related to cryptocurrencies in the Iranian and common law legal systems and analyzes the efforts of both systems in addressing the legal challenges associated with this field.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".