Bibliographic record
Abstract
Cryptocurrencies, such as Bitcoin and Dogecoin, are taking the financial world by storm. Like all financial instruments, these cryptocurrencies and the crypto exchanges they are traded on give rise to complex legal issues. This article concerns an understudied yet crucial aspect of crypto trading, namely crypto disputes and their resolution. The sheer number and growing popularity of cryptocurrencies means that disputes arising from their trading are likely to increase, yet it remains unclear how they are being, or will be, resolved. Novel issues surrounding the identity of traders, the jurisdictional limits of domestic courts, the applicable governing law(s), and complex technical evidence may mean that traditional non-binding mechanisms—such as negotiation and mediation, as well as binding legal mechanisms—such as litigation and arbitration, may be ill-equipped in the crypto context. At the same time, tailored crypto-specific dispute resolution mechanisms are only at nascent stages of development. In this article, we take a first empirical look at the mechanisms by which cryptocurrencies and crypto exchanges choose to resolve disputes with their users. We situate our results in the larger commercial dispute resolution literature and provide a preliminary account of how and why cryptocurrencies and exchanges are making these dispute resolution choices. We find that crypto platforms predominantly resort to domestic litigation and international arbitration to resolve future disputes. Moreover, we find that the ability to prohibit class proceedings seems to be the strongest explanatory factor in crypto platforms’ choice of international arbitration. In contrast, providing for the venue seems to be most strongly linked with choosing domestic litigation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".