Punishing Contract Breakers: Whiten v Pilot Insurance and the Sea Change in Canadian Law
Bibliographic record
Abstract
This article is structured into four substantive sections. It first considers the decision in Whiten v Pilot Insurance Co to establish that the decision has profoundly changed Canadian law on this issue,3 and to identify an approach to punitive damages that is preferred by this writer. Secondly, the article brit:fly surveys the availability of punitive damages for breach of contract in five jurisdictions. This is designed to establish the context for the later policy arguments with respect to punitive damages, and also to identify a trend in several jurisdictions towards a more liberal approach to the availability of such damages in contract actions. Thirdly, in recognition of the fact that continuing negativism towards punitive damages is based on the perceived strength of the policy reasons against them, the three main objections to punitive damages for breach of contract are set out: namely, the contract-tort distinction, the argument from efficient breach, and the windfall objection. This article then attempts to rebut the first two objections as being misconceived and argues that the third is not decisive against the availability of punitive damages in contract. Fourthly, the article presents three objectives of punitive damages in contract: punishment, deterrence, and denunciation. It is argued that these are valid objectives of contract law and that punitive damages are a legitimate means to achieve them.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".