Cryptocurrency and Tax Evasion: Legal Gaps and Regulatory Responses in the Post-Blockchain Era
Bibliographic record
Abstract
This paper investigated this timeless problem of tax evasion with the use of cryptocurrencies in a post-blockchain reality, in terms of legal grey zones and regulatory frameworks in various jurisdictions. Tax evaders have taken advantage of the legal grey areas, decentralized finance (DeFi) protocols, and privacy-based tools in order to hide their transactions despite the blockchain being transparent. The study used a qualitative multinational comparative research approach in which the authors have used document research and interviews with experts to examine enforcement processes in different jurisdictions including the United States, Canada, Germany, Japan, Australia and Nigeria. Conclusions were that effective statutory frameworks, high technological means of enforcement and stringent penalties were found to impact on the compliance rates positively as can be seen in Germany and Japan. Conversely, in other countries, there were high cases of non-compliance due to weakly disunity of regulation and little technological capacity, e.g., Nigeria and Canada. The introduction of DeFi became another problem since it eliminated centralized intermediaries and made the traditional tax pay reporting system more complicated. Moreover, the paper has highlighted that enforcement tactics should give due attention to enable them to balance between surveillance and privacy safeguards to keep the citizens trusty and willingly follow the law. The solution policy proposals involved integration of legal and legislative frameworks across countries globally, the integration of automated reporting solutions and investment in compliance solutions that preserve privacy. Future study on taxpayer conduct, technological breakthroughs and inter-jurisdictional cooperation to come up with resilient tax governance systems should be a matter of priority. This study added to the argument about an effective and fair establishment as an economy, as well as tax frameworks, goes digital.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".