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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".