Taking the Risk out of Termination: An Enterprise Risk Management Analysis of the Normative System of Employment Standards Challenged by Honda v Keays
Bibliographic record
Abstract
Over many years, Canadian courts have crafted a unique body of employment law jurisprudence. The purpose of this paper is to discuss how that jurisprudence was unraveled when the Supreme Court of Canada rendered its decision in Honda v. Keays. That holding, in effect, overturned aspects of Wallace v. United Grain Growers; a case that transformed how non-unionized employees were thought of and treated in Canadian society. This paper examines the analytical underpinnings of wrongful dismissal law’s previous regime on bad faith damages and juxtaposes it to the new regime in Keays, concluding that the shift in focus from employer misconduct to employee losses exacerbates the employment relationship’s power imbalance and deprives employees of protection when they need it the most. In reaching this conclusion, the paper uses the analytic frameworks of reflexive regulation and enterprise risk management to predict how employers will react to their newfound advantage. The finding is that by taking the risk out of termination, there is now downward pressure to do away with the normative “upwardly ratcheting” system of employment standards that workplaces enjoyed under Wallace.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".