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Record W3124101900

Punitive Damages: A Comparative Analysis

2004· article· en· W3124101900 on OpenAlexaboutno aff
John Y. Gotanda

Bibliographic record

VenueWorking Paper Series · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsPunitive damagesDamagesPolitical scienceLawBusiness
DOInot available

Abstract

fetched live from OpenAlex

In light of expanding international trade, it is increasingly likely that politicians, courts and tribunals will wrestle with whether punitive damages are appropriate in transnational disputes, and whether countries that traditionally do no allow exemplary relief should recognize and enforce foreign awards of such damages. Furthermore, by seeing how different systems address these problems, we can gain a deeper understanding of the role of punitive damages in our own legal system and be better able to deal with punitive damages issues in the international arena. This Article undertakes a thorough comparative study of punitive damages in common law countries. It examines the laws of England, Canada, Australia, New Zealand and the United States to determine whether there exists a consensus on the availability of punitive damages. The Article finds that, despite the controversy over the appropriateness of punitive damages, they are widely available in these countries and claims for such damages have increased in recent years. It also finds, however, that there is little consensus on the factors that are used to determine the amount of punitive damages that should be awarded. Some jurisdictions provide little or no guidance to the judge or jury who sets the award. Others provide a detailed list of factors, and one country even provides damages brackets to guide the decision maker in fixing the amount of punitive damages. The Article concludes that all countries have taken steps to rein in unreasonably large punitive damages awards. Those steps vary greatly from country to country, as do the standards for determining what constitutes an excessive award.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.056
GPT teacher head0.339
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations7
Published2004
Admission routes1
Has abstractyes

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