Causation, Contribution and Clements: Revisiting the Material Contribution Test in Canadian Tort Law
Bibliographic record
Abstract
In 2007 the Supreme Court of Canada articulated a test of material contribution to risk as an alternative to sine qua non in the Canadian law of causation. The test elucidated in Resurfice v. Hanke was designed to address situations in which application of the but-for standard would produce injustice because of the existence of intractable uncertainty unconnected to the merits of the plaintiff’s case. In scenarios involving poorly understood technologies, the quest for proof of cause on a balance of probabilities may be a Quixotic one. In such cases, it makes sense to impose liability on defendants who have negligently created a risk of the kind that ultimately materialized. The 2010 decision of the BC Court of Appeal in Clements (Litigation Guardian of) v Clements drastically narrowed the scope of the material contribution exception set out in Hanke, and replaced a principled test with an arbitrary, categorical approach. The author argues that the Hanke test strikes an appropriate balance between the interests of plaintiffs (compensation) and the public (deterrence) on the one hand, and fairness to defendants on the other.
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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 | 0.025 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.017 | 0.049 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".