Swimming Against the Tide: New Challenges for Unions Under Australian Labor Law
Bibliographic record
Abstract
In this article, we explore a number of recent developments that affect the regulatory supports for — and impediments to — union organizing and representation under Australian law. The main federal labor statute, the Fair Work Act 2009 (Austl.), provides unions with a framework of rights, including access to work sites and protections against the victimization of union activists. In some respects, these supports for union organization remain stronger than those found in the comparable labor law systems of the United Kingdom, Canada, and the United States. At the same time, however, the political tide has clearly turned against trade unions in Australia. Even under Australian Labor Party (ALP) governments, as explained by Rae Cooper and Bradon Ellem in their separate contribution to this collection, the institutional role of unions had been progressively weakened. The present conservative Liberal/National Government, wary of being seen to reduce individual protections for workers, has focused its reform efforts on union rights and activities. Besides seeking to highlight malfeasance and corruption within the administration of unions, it has attempted to truncate still further what by international standards is a severely limited right to take industrial action. Recent court rulings have also created important gaps in the protections given to union members and officials. Against that backdrop, the challenge to organize Australian workplaces has never been greater.
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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 | 0.034 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.028 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 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".