Regulating the use of Crypto-assets as collateral in secured transactions: U.S and Canadian Perspectives
Bibliographic record
Abstract
The thesis is focused on regulating the use of crypto-assets as collateral in secured transactions. The ability of a debtor to receive a loan from a lender in exchange for its collateral is a vital part of global commercial activity. Crypto-assets are a relatively new asset class introduced with the term “cryptocurrency” in the Bitcoin Whitepaper released by Satoshi Nakamoto in 2008. Since the invention of crypto-assets, many regulatory efforts have been made to govern the use of crypto-assets by different countries including Canada. These regulatory efforts have led to policies on crypto-assets in several contexts such as taxation and their use in financial crimes. That crypto-assets use in secured transactions has yet to be expressly regulated in Canada has led to speculation on how the current personal property security law in Canada applies to crypto-assets. An examination of the current personal property security law in Canada reveals that, under existing law, it is difficult to acquire a reliable and effective security interest in crypto-assets. This thesis, therefore, identifies the current issues hampering the appropriate regulation of crypto-assets under Canadian secured transaction law, evaluates the consistency of the relevant laws with guiding secured transaction values such as facility and certainty, and recommends possible solutions to the current issues. The issues include the failure to expressly categorize crypto-assets as a form of personal property, the lack of suitable modes of perfecting security interests in crypto-assets, and the non-negotiability of crypto-assets. The analysis and recommendations made in this thesis are intended to bring the issues with crypto-related secured transactions to light so that they may be considered and addressed by lawmakers in Canada. The recommendations in this thesis will also be of interest to jurisdictions with similar secured transaction regulatory frameworks, which are considering the incorporation of crypto-assets into their secured transactions legislation as a unique form of personal property.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".