De la fiducie de données en droit civil québécois : étude exploratoire pour un outil en construction
Bibliographic record
Abstract
À l’heure où l’exploitation des renseignements personnels par les organisations publiques comme privées s’intensifie, la recherche de modèles alternatifs de gouvernance des données se développe en vue d’établir un plus haut degré de contrôle et de protection des personnes concernées sur les renseignements personnels. Les pratiques numériques actuelles révèlent en effet que l’exploitation des renseignements personnels joue bien souvent au détriment des droits et intérêts des personnes concernées. Fort de ce constat, la notion de data trust a émergé au sein de diverses juridictions de common law. Celle-ci propose de recourir au droit des trusts pour affiner les modalités d’usage et d’exploitation des renseignements personnels par des tiers. Face à la récente effervescence autour de ce concept, ce texte propose d’étudier quelle pourrait être sa réception en droit civil québécois compte tenu des particularités de son modèle fiduciaire.
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 | 0.037 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".