Common-Sense Causation: How a Robust and Pragmatic Application of the 'But For' Test Can Solve the Circular Causation Problem in Cases of Multiple Contributing Tortfeasors
Bibliographic record
Abstract
The authors consider the application of the test for in complex multiple tortfeasor cases, and argue that to the extent the test appears unworkable, the problem is not the test but its overly strict, granular application. The test is intended as a helpful proxy or a convenient tool to detect whether there is a relationship between the tortious acts of a wrongdoer and the injuries of the victim sufficient to justify compensation. When applied to multiple tortfeasors on a one-by-one basis, however, it can instead become an unduly restrictive, technical requirement that frustrates the underlying goals of negligence law. The authors explore the history and purpose of the law of in Canadian tort law, as well as scenarios where the test appears to break down because of the circular causation problem, then propose that the test be applied in a robust and pragmatic manner to negligent conduct, rather than individual defendants. Applying the test in this manner, the authors submit, would ensure a connection between defendants' wrongful acts and the injuries for which the plaintiff seeks recovery sufficient to justify compensation, without creating an opportunity for defendants to escape liability by pointing the finger at other tortfeasors.
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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 | 0.061 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".