Causal Draws and Causal Inferences: A Solution to Clements v. Clements (and Other Causation Cases)
Bibliographic record
Abstract
Nothing in tort causes more confusion than causation. In early 2012, the Supreme Court of Canada will hear Clements (Litigation Guardian of) v. Clements,1 on appeal from the British Columbia Court of Appeal. At trial and appeal, this case pitted negligence law’s default “but for” test for causation against the alternative material contribution test for causation. When does a court use one test or the other? How can litigants sensibly predict case results? Clements is a case about a motorcycle accident where the passenger was severely injured because the motorcycle capsized as a result of a punctured tire. The driver was speeding in wet weather and had improperly overloaded the motorcycle. The evidence was unable to definitively explain the specific causal role that the driver’s negligence played in the motorcycle upset and resulting injuries to the passenger (who was the driver’s wife).This case is an illustration that causation is getting too confusing. Confusion breeds consistency costs. A return to first principles and simple foundational doctrine would go a long way to rehabilitating the thinking about causation and how to prove it. As will be discussed below, this case should probably not be about dueling doctrinal tests for causation at all but instead about how courts determine evidentiary sufficiency for causal proof.
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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 | 0.028 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 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".