Cryptocurrency in the Darknet: sustainability of the current national legislation
Bibliographic record
Abstract
Purpose The purpose of the study is to show that divergent perceptions among regulators, the regulated and the associated regulatory bodies across multiple jurisdictions regarding the nature and functionality of cryptocurrencies hamper the development of a more comprehensive and coherent regulatory framework in curbing crimes and other related risks associated with cryptocurrencies. Design/methodology/approach The study has used a descriptive doctrinal legal research method to investigate and understand the insights of existing laws and regulations in four selected jurisdictions concerning cryptocurrencies and how these laws could be further improved and developed to reduce crypto-related crimes. Furthermore, the study has also used a comparative research method to conceptualize the contours of the new legal discourse emerging from cryptocurrencies to adopt and implement a sound regulatory framework. Findings The study illustrated that divergent regulatory treatment among different jurisdictions might suffocate novel digital innovations such as cryptocurrency. These fragmented regulatory approaches by various jurisdictions question the sustainability of the present national legislation adopted to regulate cryptocurrencies. Looking into other jurisdictional developments in regulating cryptocurrencies, it is apparent that a concerted regulatory approach is needed to minimize the abuse of this innovation. Research limitations/implications The study has implications for regulators and policymakers to review the current regulatory framework for regulating cryptocurrencies to prevent regulatory arbitrage. The divergent legislative measures concerning cryptocurrency among different jurisdictions question the sustainability of these legislative initiatives, considering the evolving and borderless nature of cryptocurrency. Therefore, this paper will help regulators to consider the present legislative gaps in establishing a common global regulatory approach in the crypto sphere. Originality/value The study contributes to the existing body of literature by examining the regulatory frameworks of four jurisdictions, namely, the USA, Canada, China and the EU, related to cryptocurrencies, with a discussion on the development of cryptocurrencies-related laws among these four jurisdictions and their sustainability in curbing crimes in the Darknet.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".