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Tort Liability Under Uncertainty

2001· book· en· W570441982 on OpenAlex
Ariel Porat, Alex Stein

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOxford University Press eBooks · 2001
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsnot available
Fundersnot available
KeywordsTortLiabilityActuarial scienceBusinessLaw and economicsEconomicsAccounting

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.896
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.192
Teacher spread0.157 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it