The Feasibility of Addressing the Financing of Terrorist Crimes in the Realm of Cryptocurrencies: The Experiences of Iran and Other Countries
Bibliographic record
Abstract
Cryptocurrencies, despite their ambiguous nature, represent one of the most significant new phenomena in the global economy. Since the introduction of the first cryptocurrency, Bitcoin, in 2009, terrorist groups have increasingly utilized these currencies to finance their activities. Consequently, states, organizations, and financial institutions have been compelled to adopt effective anti-terrorist financing strategies in response to this development. This research examines a range of issues related to cryptocurrencies, their utilization in terrorist financing, and the associated benefits and risks. Within the context of Iran's regulatory framework, existing policies and measures to combat the financing of terrorist crimes through cryptocurrencies have led to challenges characterized by conflicting and fragmented approaches to the regulation of cryptocurrency exchanges and mining, both theoretically and practically, which include the illegitimacy of exchange activities. Internally, the Central Bank has issued directives aimed at clarifying this phenomenon and has sought demands from higher authorities, particularly the Islamic Council. In contrast, other countries, such as China, have adopted a dual policy, prohibiting the use of cryptocurrencies in monetary and banking contexts. Notably, nations like Canada and the United States have established specific legal regulations and policies governing Bitcoin usage, while Japan has developed regulations for virtual currency exchange service providers, including mechanisms for identifying violators through guaranteed criminal enforcement.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".