Is Class Action a Preferable Remedy for Independent Contractors? A Case Study on the Proposed Canadian Hockey League Class Action
Bibliographic record
Abstract
Abstract: A number of putative actions have been filed recently that allege misclassification of employees as independent contractors. Left unchanged, such misclassification prevents the formation of unions and avoids the application of employment standards legislation. Using the putative Canadian Hockey League class proceeding (the CHL case) as an example, this paper argues that a class action is a preferable remedy for misclassified employees because it is a more discreet option in terms of workplace politics, and it offers the hope of systemic change. The paper first reviews the 2016 Mayotte v Ontario case to highlight challenges that independent contractors face in establishing that their employer acted unreasonably. Second, the paper provides background about the CHL case and discusses the likely players’ status under the current law. Third, it reviews the law on certification of employment-related proceedings, and its likely application to the CHL case. It also looks at challenges that arise in staying a proceeding involving multijurisdictional class actions, particularly in Quebec with new article 577 of that jurisdiction’s new Code of Civil Procedure. The paper concludes with a discussion on how the CHL class action will shape hockey, the sport that defines Canada’s culture and identity.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".