MULTIPLE REASONABLE BEHAVIORS CASES: THE PROBLEM OF CAUSAL UNDERDETERMINATION IN TORT LAW
Bibliographic record
Abstract
ABSTRACT This article introduces a significant yet largely overlooked problem in the law of torts: causal underdetermination. This problem occurs when the causal inquiry of a but-for test produces not one but two results, which are contradictory. According to the first, the negligent defendant is the likely cause of the plaintiff's injury, whereas according to the second, she is not. The article explains why causal underdetermination has escaped the radar of tort scholars and is perceived by courts as lack of causation. It demonstrates that the current practice in cases of causal underdetermination might lead to erroneous decisions, absolving negligent defendants of tort liability even when the evidence suggests that they are in fact the likely cause of the plaintiff's injury. This, in turn, the article asserts, may not only lead to underdeterrence among potential defendants, but also encourage manipulative litigation strategy to escape liability in retrospect. The article then proposes solutions that contend with causal underdetermination and resolve the difficulties that the current practice entails.
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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 | 0.074 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".