CODE VERSUS PRECEDENT: BLOCKCHAIN-DRIVEN GOVERNANCE AS A RESPONSE TO THE CRISIS OF TRUSTS IN MODERN CANADIAN FINANCE
Bibliographic record
Abstract
Canada's trust law is caught in a structural crisis, torn between the rigid formalism of common law and the need for adaptive governance in a digitalized global economy. Drawing on Frederick Schauer's theory of "rules as exclusionary reasons" and Douglass North's concept of "path dependence," this paper argues that Canada's regulatory framework—exemplified by Section 122 of the Income Tax Act and Section 56 of the Ontario Securities Act - prioritizes procedural compliance over substantive resilience, leading to systemic failures such as the collapse of Penn West Petroleum Trust and the judicial rejection of cryptocurrency trusts in QuadrigaCX. Through case studies and comparative analysis, we demonstrate how Canada's adherence to outdated doctrines undermines both domestic stability and international alignment with OECD standards. We propose a dual-track solution: legislative modernization through a Uniform Digital Trust Act and the integration of blockchain-based smart contracts (e.g., ERC-1400) to encode fiduciary duties into programmable legal structures. This approach not only resolves the Schauer-North paradox but also positions Canada as a leader in responsive, technology-driven trust governance.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".