De La Théorie des Troubles de Voisinage en Matière de Recours Collectif; Un Débat Clos?
Bibliographic record
Abstract
While the neighbourhood annoyance theory can be traced back over a hundred years, it was not until the Civil Code of Quebec that the legislature created a specific article to deal with it. Despite the fact that the theory is over a century old, authors and judges have yet to agree on whether article 976 C.c.Q. requires the claimant to establish fault, or if it provides for no-fault liability. In the meantime, a new question has been raised regarding the application of the neighbourhood annoyance theory in the context of a class action lawsuit. This article studies the relationship between these two concepts, and concludes that, contrary to two recent decisions from the Quebec Court of Appeal, neighbourhood annoyances can be used in class action lawsuits and no proof of fault is required.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".