Privacy, Trusts and Cross-Border Transfers of Personal Information:\nThe Quebec Perspective in the Canadian Context
Bibliographic record
Abstract
This paper argues that data protection laws apply to prevent the disclosure of certain information relating to trusts, which are increasingly being used .as business and investment vehicles. Given the broad scope of the concept of "personal information" found under both provincial and federal personal information protection statutes, arguments can be made that information relating to trust beneficiaries or trustees, where such beneficiaries or trustees are natural persons, enjoy some level of protection. Even where a trust contains an express choice of law clause providing that the laws of another province or country apply, Quebec conflict of laws rules may point to the application of Quebec's own personal information protection legislation. Hence, in order to avoid liability, trustees should use caution before disclosing trust-related information where part of the trust's business operations is outsourced to foreign jurisdictions, or where a foreign authority may request the disclosure of such information.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".