York University and Cupe 3903— Future of Collective Bargaining Rights
Bibliographic record
Abstract
The Canadian Union of Public Employees (CUPE) 3903 view the introduction of the new Ontario government’s ‘back to work’ legislation (Bill 2) as detrimental and a threat to their fundamental bargaining rights. Why is the government legislating the CUPE members back to work instead of allowing them to further negotiate with the administration of York University? If resolving issues through the collective bargaining process is not allowed, then what will happen to the relationship between the union and the university authorities? Dilemma Why did the government bring in the bill with ‘back to work’ clause? The concerned parties could have settled the issue through negotiation. Theory: Labour relations, integrative vs. distributive negotiation and third-party interventions Type of the case: Decisional and applied Protagonist: Not needed Options Negotiation, appointing mediator, adopting competitive distributive strategy or collaborative integrative strategy Discussions and Case Questions Suggest a more effective way to resolve the crisis. What are the main contentions of CUPE 3903 and the York University Administration in this negotiation? Discuss the strategies and tactics as used by CUPE 3903 and the York University Administration. Could these be optimized further? Evaluate the gap between the current strategy and the optimal one Is there any way in which a third-party mediator could craft a mutually beneficial agreement?
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 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".