Punitive Damages Revisited: Taking the Rationale for Non-Recognition of Foreign Judgments Too Far
Bibliographic record
Abstract
Punitive damages have been a controversial aspect of U.S. law; often criticized both at home and abroad. Neither U.S. law on punitive damages nor the foreign climate regarding their reception has remained static. This article notes the continuing legislative attack on punitive damages in the United States at both the state and federal level, and focuses on recent developments in case law and treaty negotiations concerning the reception of punitive damages abroad. The article begins with a brief review of the background against which current punitive damages law in the United States continues to operate, followed by consideration of the continuing evolution of U.S. Supreme Court jurisprudence on punitive damages. The Beals case in the Supreme Court of Canada and new uniform Canadian legislation on the enforcement of foreign judgments demonstrate two very different approaches to U.S. punitive damages by foreign courts. The issue is also the focus of Article 11 of the new Hague Convention on Choice of Court Agreements, which offers a much more moderate approach than the Canadian uniform act, which, if widely adopted, would constitute a major step back in terms of predictability in business and judicial relationships.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.028 | 0.039 |
| 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".