Catch me if You Can: Resolving Bitcoin Disputes with Class Actions
Bibliographic record
Abstract
ABSTRACT: In the last decade, a new kind of financial technology or “fintech” has emerged, bringing with it a host of legal issues. The most commonly known cryptocurrency, Bitcoin, is touted as the alternative to traditional money systems. Dozens of exchanges have emerged that can be used to store and transfer Bitcoins between virtual wallets. These exchanges are prone to being hacked, however, and without the infrastructure to back the “currency,” users have frequently lost Bitcoins to virtual thieves and been unable to recover their losses. This paper argues that class actions are an effective avenue for remedy against an exchange that has negligently lost Bitcoins. It provides a brief overview of Bitcoin’s underlying technology, the blockchain on which transactions are recorded, and the exchanges out of which they operate. Canadian class actions law is examined in the context of Bitcoin hacks to demonstrate how large-scale litigation can play an increasing role in fintech. There are many examples of cyber attack theft where class actions are the only viable remedy, given the commonality of harm, enormous aggregate losses, and lack of other recourse in an unregulated and uninsured industry. There are also inherent enforcement challenges that need to be addressed by regulators, such as jurisdiction conflict and party anonymity. New technology is constantly emerging and difficult to legally classify. Nevertheless, the paper concludes that class actions law is the best means of protecting consumer interests against fintech risks and supporting the objectives of access to justice, judicial economy, and behaviour modification.
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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 | 0.016 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".