Bibliographic record
Abstract
This article aims to summarize the present situation with regards to the use of cryptocurrency as collateral in secured transactions in the United States, Canada, the United Kingdom and France, and offer solutions to issues related to the use of cryptocurrency for this purpose. These proposed solutions are arranged as a framework that could be enacted in Canada, and elsewhere. The article first reviews the concept of a cryptocurrency, with special emphasis on bitcoin, and the concept of secured lending. Then, it discusses the categorization of bitcoin in the United States, Canada (with Ontario and Quebec as examples), United Kingdom and France. At this time, only the United States and Ontario have doctrinal and regulatory guidance when using cryptocurrency specifically for secured lending. Finally, this article proposes a legislative framework to take security interests in cryptocurrency in Canada, including drafts of specific statutory amendments for both Ontario and Quebec legislation. The article concludes by noting how this framework can be replicated elsewhere, notably in the United States, the United Kingdom and France.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".