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Record W2291541762 · doi:10.3138/utlj.3155

An economic analysis of waiver of tort in negligence actions

2016· article· en· W2291541762 on OpenAlexvenueaboutno aff
Edward Iacobucci, Michael J. Trebilcock

Bibliographic record

VenueUniversity of Toronto Law Journal · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsnot available
Fundersnot available
KeywordsWaiverPlaintiffTortDoctrineDamagesLawContext (archaeology)Law and economicsLiabilityDeterrence theoryContributory negligenceStrict liabilityPolitical scienceBusinessEconomics

Abstract

fetched live from OpenAlex

The legal status, scope, and policy implications of the waiver of tort doctrine have been a prominent, controversial, and unresolved feature of many recent class action proceedings in Canada, especially in products liability cases, where plaintiffs have sought to claim disgorgement of profits or revenues as an alternative to proof of damage or injury. The doctrine in recent years has been invoked frequently by plaintiffs in negligence class actions, though its legal status in such a context remains uncertain, as it has not been judicially decided. The article assesses the waiver of tort doctrine from an economic perspective. It argues that decoupling remedies from actual damages in negligence cases generally, or in products liability cases in particular, is not well founded from an economic perspective. Theory and evidence suggests that the deterrence and insurance aspects of negligence law are best achieved by linking remedies to losses actually suffered. In many contexts, disgorgement of gains from activities entailing negligent conduct may induce socially wasteful forms of over-deterrence. Economic analysis does not support the waiver of tort doctrine.

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.

How this classification was reachedexpand

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.211
Teacher spread0.189 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2016
Admission routes2
Has abstractyes

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