Collaboration between unions in a multi-union, non-exclusive bargaining regime: What can Canada learn from New Zealand?
Bibliographic record
Abstract
The Canadian union certification system guarantees workers rights to organise, bargain collectively, and strike only when a majority of co-workers favours unionisation. This contravenes International Labour Organisation standards, in which the freedom to associate is unqualified by majority support. In recent years, the Supreme Court of Canada has drawn on ILO principles to interpret constitutional rights as covering organising and collective bargaining activities related to freedom of association under section 2(d) of the Charter of Rights and Freedoms. However, it has not as yet ordered Canadian governments to enact labour relations laws consistent with these new constitutional rights. Neither has there been a general call for such legislative change. Instead, many fear that statutory support for non-majority unionism would lead to multi-union representation and intensified inter-union competition, but fail to consider that sharing the workplace might actually promote inter-union cooperation against a common adversary in management. This study addresses this shortcoming by looking at the extent and nature of inter-union collaboration in New Zealand, where non-majority, non-exclusive representation exists already. Collaboration was found to be common, not only over bargaining and lobbying, but also in organising. Implications for Canada are explored.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".