Deterring Compensation: Class Action Litigation and Damage Awards Against Corporate Defendants
Bibliographic record
Abstract
Abstract: The class action has been lauded as an efficient and effective procedural mechanism to modify a defendant’s wrongful behaviour and compensate the plaintiff. A damage award against the defendant may modify behaviour. If fault is established at a class action trial on the merits, the damage award forces the defendant to internalize the costs of its “wrongful” conduct, and simultaneously compensates the plaintiff. The recent Létourneau c JTI-MacDonald Corp decision in the Superior Court of Quebec appeared to support the theory that class actions achieve deterrence: Riordan J awarded the largest damage award in Canadian legal history against the three tobacco defendants. Although the damage award reflected the level of fault of the companies and was aimed at compensating the plaintiffs, the true costs of the award showed how compensation and deterrence in this case were inadequate and ineffective. This paper argues that the unique nature of the class action renders large damage awards counterproductive in achieving deterrence and compensation, particularly against corporate defendants. The fiduciary duty that directors owe to their corporations makes the deterrent effect of class actions questionable. Moreover, the fiduciary duty brings into question the specific faults alleged against the corporations and highlights an inherent contradiction in the law. Even if deterrence is not the primary aim in one particular case, the Létourneau decision illuminates a paradox of compensatory damages: not truly compensating. Therefore, while the plaintiff “won” the action, it also lost. The class action, at least in this circumstance, is just deterring compensation.
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".