Commentaire d’arrêt : St-Germain c Benhaim – Un Jugement Audacieux et Habile (Case Comment: St-Germain v. Benhaim – A Bold and Clever Decision)
Bibliographic record
Abstract
French Abstract: Le 5 decembre 2014, la Cour d’appel du Quebec rendait une decision majeure, St-Germain c Benhaim, 2014 QCCA 2207, portant sur le lien de causalite entre l’omission d’effectuer des tests et prendre d’autres mesures qui auraient permis de diagnostiquer le cancer d’un patient en temps utile et son deces eventuel. Dans son jugement, la majorite de la Cour d’appel base une inference quant a la causalite medicale sur le seul fondement d’une donnee statistique, justifiant cet allegement de preuve par le fait que les defendeurs ont, par leur faute, empeche les demandeurs de prouver la causalite. Le commentaire cherche a montrer comment cette decision fait preuve d’audace en s’eloignant de l’orthodoxie ambiante et manipule les concepts pertinents de facon habile.English Abstract: On 5 December 2014, the Quebec Court of Appeal rendered a groundbreaking decision, St-Germain c Benhaim, 2014 QCCA 2207, regarding the causal link between, on the one hand, an omission to conduct tests and take other steps that could have enabled a patient’s cancer to be diagnosed in due time, and on the other hand, his eventual death. In its judgment, the majority of the Court of Appeal drew an inference of medical causation on a single statistic, and justified this relaxation of the evidentiary burden on the grounds that the defendants, through their own fault, had prevented the plaintiffs from establishing causation. This case comment seeks to demonstrate the boldness of the decision in distancing itself from established case law orthodoxy and its skillful handling of the relevant concepts.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.046 | 0.051 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".