Revue des décisions marquantes de l’année 2020 en matière de vie privée et de protection des renseignements personnels
Bibliographic record
Abstract
RESUME Cet article analyse certaines decisions marquantes en matiere de vie privee et de protection des renseignements personnels rendues au cours de l’annee 2020 ou a la fin de l’annee 2019. Fruit d’une annee hors du commun, cette revue jurisprudentielle s’avere variee tant du point de vue des themes abordes (biometrie, pratiques commerciales trompeuses, securite de l’information, actions collectives, reputation en ligne) que de celui des autorites impliquees dans les decisions (Cour superieure, Commission d’acces a l’information, Commissaire a la protection de la vie privee du Canada, Bureau de la concurrence). ABSTRACT This article analyzes some of the landmark privacy and data protection decisions that were issued in 2020 or in late 2019. Being the product of an unprecedented year, this case law review proves to be diverse both in terms of issues (biometrics, deceptive marketing practices, information security, class actions, online reputation) and authorities involved (Superior Court, Commission d’acces a l’information, Office of the Privacy Commissioner of Canada, Competition Bureau).
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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".