Landmark Cases in the Law of Punitive Damages
Bibliographic record
Abstract
Punitive damages are private law’s most controversial remedy. This book traces the development of the jurisdiction from the foundational decisions of Huckle v Money and Wilkes v Wood in England, to leading modern cases such as Harris v Digital Pulse Pty Ltd in Australia, Whiten v Pilot Insurance Co in Canada, Couch v AG (No 2) in New Zealand, PH Hydraulics & Engineering Pte Ltd v Airtrust (Hong Kong) Ltd in Singapore and Mathias v Accor Economy Lodging, Inc and State Farm Mutual Automobile Insurance Co v Campbell in the United States. Many of the decisions addressed are not only landmarks regarding punitive damages but are among the most important judgments delivered concerning private law more generally. The essays, which are written by leading scholars from a wide range of jurisdictions, cast new light on the cases covered. They do so by examining their historical antecedents and the impact that they have had on the development of the law. The full spectrum of issues regarding punitive damages is addressed including the insurability of punishment, constitutional constraints on the remedy’s availability and whether the award should be confined to particular causes of action. The collection will be of interest to all scholars and students of private law. It concentrates on common law cases although civilian perspectives, drawn from France and Germany, are also offered.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".