Bibliographic record
Abstract
Purpose To address recent cases and the applicable legal principles relating to cryptocurrency, and to contribute to legal thought in this developing area of law. Design/methodology/approach This article considers recent cryptocurrency related cases in Singapore, Canada and the United Kingdom, and then considers the implications of the developing law in relation to proper causes of action and issues of practical asset recovery relating to the enforcement of judgments. Findings The intangible and highly movable nature of cryptocurrency places a premium on decisive asset recovery. The cases also suggest that injunctions remain a useful and effective debt recovery tool, especially when coupled with quick investigative action to trace cryptocurrency payments. However, the law remains unsettled as to the most appropriate cause of action for a claim in cryptocurrency or how a debt in cryptocurrency can be subject to execution. These issues raise the fundamental question of the nature of cryptocurrency, whether it belongs to an existing category of property, or if it is sui generis. Practical implications Cryptocurrency remains relatively novel and usage is increasing but not widespread. Users of cryptocurrency and lawyers involved in transactions or disputes involving cryptocurrency would benefit from a broader understanding of the legal issues Originality/value This article provides expert analysis from experienced litigation lawyers familiar with the concepts behind cryptocurrency.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".