Vie privée et protection des renseignements personnels : revue de la jurisprudence 2017
Bibliographic record
Abstract
Le present article analyse plusieurs decisions en matiere de vie privee et de protection des renseignements personnels, presentee par priorite et axes d’interet. Nous debuterons par les enjeux de vie privee et de messages textes (axe 1), puis par les questions de juridiction (axe 2) et de territorialite (axe 3), nous poursuivrons sur l’angle de la concurrence (axe 4) pour finir sur les aspects anti-pourriel (axe 5). Cette structure jalonne selon nous les moments importants de l’annee 2017 en matiere de vie privee et de protection des renseignements personnels – etant entendu que d’autres decisions et enquetes, notamment du Commissariat a la protection de la vie privee du Canada et de la Commission d’acces a l’information du Quebec, soulevent egalement des questions interessantes, mais ecrire, c’est choisir, et choisir, c’est renoncer. The present article analyzes of several decisions regarding privacy and protection of personal information, presented by priority and axes of interest. We will begin with issues regarding privacy and text messages (axis 1), then questions of jurisdiction (axis 2) and territoriality (axis 3), we will continue on the angle of competition (axis 4) to finish with anti-spam aspects (axis 5). In our opinion, this structure lays out the key moments of 2017 in terms of privacy and protection of personal information – it being understood that other decisions and investigations, in particular by the Office of the Privacy Commissioner of Canada and the Quebec Access to Information Commission, also raise interesting
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.064 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 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".