Clements v. Clements: A material contribution to the jurisprudence - The Supreme Court of Canada clarifies the law of causation
Bibliographic record
Abstract
The recent Supreme Court of Canada decision Clements v. Clements[1] provides important guidance on the appropriate application of the material contribution test in cases of negligence. This case commentary will provide an overview of the material contribution and “but for” tests of causation, outline the Supreme Court’s reasoning in the decision, and analyze its broader implications. It is suggested that the Court has significantly clarified the law of causation, emphasizing the necessity of utilizing the new “global but for” test, while leaving room for the application of the material contribution test in (as yet to be seen) appropriate circumstances.\nCopyright © 2012 by Dr. Emir Crowne & Omar Ha-Redeye \n* Dr. Emir Crowne, BA, LLB, LLM, LLM, PhD, Associate Professor, University of Windsor, Faculty of Law, and, Omar Ha-Redeye, AAS, BHA, PGCert, JD, LLM (cand.), Partner, Fleet Street Law. But for the exceptional editorial assistance of Lida Moazzam and the material contribution of the Law Foundation of Ontario this article could not have been produced. Both are gratefully acknowledged.\n[1] Clements v Clements, 2012 SCC 32 [Clements].
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.017 | 0.014 |
| 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".