L'industrie de l'omniscience : le profilage comportemental et le droit à la vie privée au Canada
Bibliographic record
Abstract
La collecte et l’agrégation des renseignements personnels par des organisations du secteur privé représentent une menace grandissante pour la vie privée des citoyens canadiens. Bien que les pratiques de profilage à des fins commerciales soient en pleine émergence depuis l’arrivée d’Internet, le Canada dispose tout de même de mesures législatives servant à limiter leur impact sur la vie privée des individus. Cependant, certaines organisations parviennent néanmoins à contourner ces mesures législatives par l’entremise d’ententes contractuelles auxquelles adhèrent les utilisateurs d’Internet. Il est donc indispensable que les lois en matière de protection des renseignements personnels soient modernisées afin de minimiser les impacts du profilage en ligne. À cet effet, certaines leçons peuvent être tirées de l’approche européenne en matière de protection des renseignements personnels collectés à partir d’Internet et d’autres technologies d’information et de communication.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".