The Lack of Credibility on the Web and Its Legal Ramifications Under Quebec's Consumer Protection Act
Bibliographic record
Abstract
Cet article examine les complications juridiques mises de l’avant par la récente étude « Credibility on the Web » publiée par le groupe Consumers International. Selon cette étude, plusieurs sites n’étant affiliés à aucun manufacturier ou vendeur font leur apparition sur le Web afin d’offrir aux consommateurs certaines informations générales concernant divers produits et services. Malheureusement, puisque ces sites sont gérés par des tiers n’étant pas partie à la relation consommateur-commerçant, une nouvelle problématique juridique peut être mise de l’avant : quelle protection accorder aux consommateurs eu égard à leurs achats effectués suite aux mauvais conseils de ces sites ? Ce texte vise à mettre sur pied les fondements d’une telle problématique selon le droit de la consommation québécois et à envisager certaines pistes de solutions possibles concernant les recours que pourraient avoir les consommateurs contre de tels sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".