Collective Bargaining Beyond the Boundaries of Employment: A Comparative Analysis
Bibliographic record
Abstract
Labour laws are designed in part to provide workers with adequate minimum labour standards, including access to collective voice and representation in establishing working conditions. They generally focus on 'employees' but an increasing number of workers are not employees. As Judy Fudge has observed, 'the forms of work and numbers of workers outside the scope of labour law has proliferated'. For example, in highly developed countries, self-employment ranges from 'freelance professionals to women who provide childcare in their homes, and to men who drive trucks or operate franchises for a living'. Collective bargaining laws that only empower 'employees' (which may be narrowly defined) to organise and bargain as a collective may leave many workers subject to more restrictive rules of contract, commercial and competition law. This article examines how jurisdictions in Australia and Canada have dealt with the question of collective bargaining by self-employed workers. The article develops a typology of regulatory models, outlining the features of each type of model and considering, in particular, the manner in which these models differ from widely applicable models of employee collective bargaining. It assesses the strengths and weaknesses of the models from the perspective of facilitating worker access to collective bargaining.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".