Cryptocurrency as Compensation: Legal and Economic Aspects for Labor Remuneration
Bibliographic record
Abstract
Abstract Subject and purpose of work This article presents a comprehensive analysis of the use of cryptocurrencies for salary payments, examining both economic and legal dimensions. As digital assets gain traction globally, their integration into payroll systems raises critical regulatory, financial, and social considerations. Materials and methods Adopting an interdisciplinary approach, the study evaluates the implications of cryptocurrencies’ inherent characteristics – such as volatility, decentralization, and regulatory ambiguity – on their feasibility as a payment method. Results Special attention is given to employer and employee protections, comparing legislative frameworks from the European Union (MiCA), Canada, and the United Kingdom. The research highlights the need for regulatory harmonization and explores the potential of centralized cryptocurrencies and CBDCs as stable alternatives. Findings underscore the strategic importance of transparent governance and legal safeguards to mitigate risks, ensuring financial security for market participants. Conclusions The study concludes with recommendations for policy development aimed at facilitating the responsible adoption of cryptocurrency-based remuneration while addressing socio-economic challenges.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".