Fiduciary Access to Digital Assets: A Review of the Uniform Law Conference of Canada's Proposed Uniform Act and Comparable American Model Legislation
Bibliographic record
Abstract
No jurisdiction in Canada has yet enacted comprehensive legislation regarding fiduciary access to the digital assets of an individual who has died, become incapacitated, or has appointed an attorney or other representative. In August, 2016, the Uniform Law Conference of Canada (ULCC) adopted a uniform Act on fiduciary access to digital assets (ULCC Uniform Act). This paper discusses why there may be a need for legislation, and then examines the most important elements of the ULCC Uniform Act. The Act, which tends to favour fiduciary access and media neutrality, is compared throughout the paper with the two American Acts prepared by the American Uniform Law Commission. The first American Act was adopted in 2014 and then withdrawn due to concerns voiced by internet service providers and civil liberty groups regarding privacy issues, and the other, a revised version, was subsequently adopted in 2015.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.032 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".