Simplifying the Complexities of Negligence Law – A Joint Academic/Judicial Proposal
Bibliographic record
Abstract
Abstract Over a century, common law judges, academics, and practitioners have struggled with the complexities of negligence law. All agree that negligence liability is imposed on a defendant whose unreasonable conduct caused foreseeable harm to the plaintiff, and who owed a duty of care to the plaintiff. But views differ considerably as to the meaning and role of each element (unreasonable conduct, harm causation, duty), the test and the relevant considerations that should be applied to each, the interrelation between these elements, and the meaning and role of the foreseeability requirement in each element. Against this background, the author has argued for years that the above complexities can be easily solved by a simplified model of negligence. Recently the author’s model has been embraced by Israeli justices and judges. The article presents the proposed model, explains how it solves the described complexities, and fends off criticism. It then demonstrates the model’s operation by applying it to the 2018 SCC’s decision in the Rankin case. A glimpse at the Third Restatement on Torts shows that it steers in the same direction, as evidenced by an analysis of the Palsgraf case and the unforeseeable plaintiff question. Following a short overview of leading British cases from Donoghue to the 2018 decision in Robinson, it is argued that a shift to the proposed model would be a natural evolution that can be easily achieved. In contrast, it is argued that Canadian law has moved in another direction, for incorrect reasons. The model is then compared with another reform recently suggested in the literature. Finally, fault-based liability in continental Europe is viewed from the perspective of the proposed model.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.014 | 0.012 |
| 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".