Bargaining Sectoral Standards: Towards Canadian Fair Pay Agreement Legislation
Bibliographic record
Abstract
In response to the need for more inclusive collective bargaining legislation to combat inequality and improve conditions in the workplace, this paper considers the recently introduced New Zealand Fair Pay Agreement [FPA] sectoral bargaining framework and offers a preliminary series of ideas and proposals setting out how an FPA model for bargaining sectoral standards could work in Canada. It is intended as the beginning of a more detailed discussion on the development of an FPA regime culminating in model legislation that could be adapted to different Canadian jurisdictions. Guided by principles of accountability, integration, and inclusivity, this proposal is intended to apply to all workers in an employment relationship – including dependent contractors and gig and platform workers. The proposed system is to be structured as a new, stand-alone statute, drawing upon existing institutions administering collective bargaining legislation, incorporating some familiar collective bargaining concepts: good faith bargaining, dues check-off, and unfair labour practice protection. It is intended to preserve existing collective bargaining arrangements by excluding specified sectors with existing high union density or existing sectoral bargaining. However, it is also intended to offer a new, sectoral bargaining option based on industry or occupation sectors, producing FPA “sector agreements” containing minimum standards applying to all employees and employers in the sector. This proposed framework would operate in parallel and in conjunction with the existing enterprise-level collective bargaining system.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".