You Can't Fire Me: The Problems with Wrongful Dismissal Damages in Canada
Bibliographic record
Abstract
The assessment of wrongful dismissal damages in Canadian law has long been governed by the principles established in Bardal v Globe & Mail Ltd. Although this model of analysis has been met with near universal approval in every decision-making forum in Canada, the principles underlying Bardal warrant further discussion. This work focuses on the contractual core of employment disputes and analyzes the interpretive framework for common law claims for wrongful dismissal. It will show that the traditional law of private remedies has been distorted in the context of wrongful dismissal as a result of the wholesale adoption of the Bardal Factors. The Bardal approach requires courts to depart from traditional doctrines of remedies and contract law and extends beyond the traditional goal of restitutio in integrum to a distributive theory of damages. This ultimately results in outcomes that depart from the original goal of wrongful dismissal remedies: protecting society’s vulnerable workers from unfair employment practices. This paper will suggest that damages from the breach of the employment contract should be objectively assessed from the intention of the parties at the time of contract formation. The courts in Lazarowicz and Bartlam have provided useful guidance with respect to how this method can be successfully applied. Returning to the goal of restitutio in integrum and corrective justice in wrongful dismissal damages would provide needed predictability and stability in employment 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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.032 | 0.010 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".