Applying the Principle of Proportionality in Employment and Labour Law Contexts
Bibliographic record
Abstract
The principle of proportionality, which is designed to limit abuse of power and infringement of human rights by governments and legislatures, has become a fundamental and binding legal principle in the jurisprudence of many countries. Ever since the seminal R. v. Oakes decision, when the Supreme Court of Canada interpreted section 1 of the Canadian Charter of Rights and Freedoms as entailing a three-step proportionality test, proportionality has become an important pillar of Canadian law. This article argues that the principle of proportionality actually extends, and should extend, to the private sphere—imposing limitations on employers and trade unions when using their powers. It first argues, at a descriptive level, that proportionality already plays a significant role (although often not explicitly) in various Canadian labour and employment law contexts, a role not sufficiently acknowledged thus far. It then turns to the normative level and explores the justifications for extending the application of proportionality to the private sphere and more specifically to the employment relationship. The article advocates a more explicit use and a structured application of the three-stage proportionality test in various employment and labour law contexts.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.062 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| 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".