Trade Union Recognition and the Concepts of ‘Workplace’ in South Africa and the ‘Appropriate Bargaining Unit’ in Canada: Some Comparative Insights into Bargaining Constituencies and Protection of Minority Interests
Bibliographic record
Abstract
This article identifies challenges surrounding the legal regulation of trade union recognition with specific reference to the concept of ‘workplace’ as the constituency within which the majority rules. The definition of ‘workplace’ in the South African Labour Relations Act 66 of 1995 creates the potential for minority interests to be ignored and hinders even majority unions seeking recognition, especially where the workplace is dispersed across different locations. This predicament, and the harsh effect and potential dangers of excluding minority voices in the labour context, has also been recognised by the Constitutional Court. This article considers two factors central to Canada’s independently determined ‘appropriate bargaining unit’: accommodating special or significant minority interests and addressing recognition in the context of multi-location employers. Although the Canadian legal system (like the South African one) favours majority unions, this article seeks to show Canadian law shows greater awareness of the potential unfairness of unqualified or misapplied majoritarianism. It highlights Canada’s independently determined bargaining unit as the constituency for majority rule and concludes that this model may offer a more appropriate framework for South Africa.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.029 | 0.016 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".