Bibliographic record
Abstract
Uncertainty is present in virtually every tort litigation. Generally, courts tackle the uncertainty problem by requiring the plaintiff to prove his case by the preponderance of the evidence. However, on numerous occasions tort plaintiffs encounter systematic difficulties in establishing their allegations against defendants. This phenomenon is prevalent in the area of mass torts, which has occupied the centre of the tort law agenda in the past three decades. In this area, victims of torts systematically fail to establish their lawsuits against wrongdoers even when it is clear that the latter are responsible for enormous damages. The uncertainty problem is not limited to the mass tort context. In many other contexts, tort and evidence law doctrines also fail to offer satisfactory solutions to that problem. Typically, this failure occurs in cases that involve indeterminate causation, an evidentiary barrier that prevents factual attribution of the litigated damage to the defendant’s wrongdoing. Due to this failure, victims of torts are left under-compensated and their wrongdoers under-deterred. This book provides a treatment of the problem of uncertainty in torts at both doctrinal and policy levels. It presents and critically examines the existing doctrinal solutions of the problem. It also offers a number of original solutions to the problem, such as imposition of collective liability and liability for evidential damage. The book combines the traditional doctrinal depiction of the law, as evolved in England, Canada, United States, and Israel, with general theoretical insights that include economic analysis.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".