Collective Bargaining in Fissured Work Contexts: An Analysis of Core Challenges and Novel Experiments
Bibliographic record
Abstract
Abstract Facilitating access to effective and meaningful collective bargaining is at the heart of the most recent set of reforms to the Fair Work Act 2009 (Cth) (‘FW Act’) enacted in the Fair Work Legislation Amendment (Secure Jobs, Better Pay) 2022 (Act). In the shadow of these reforms, this article explores who can engage in collective bargaining in Australia and under what conditions. While there are a range of issues impeding the effectiveness of the collective bargaining system under the FW Act, this article focuses on the question of bargaining access under both labour and competition laws and reveals some of the formidable challenges facing employed and non-employed workers alike. It examines how the rise in dependent contractors and the disaggregation of firms—through labour hire, subcontracting, franchising and/or digital platforms—has destabilised the binary conception of employment. The decline in formal employment and the growth of the ‘fissured workplace’ have not only perpetuated the problem of ‘wage theft’, they have altered the way in which wages are set in the first place. Moreover, these factors have exposed the tensions that lie between the regulation of mainstream labour markets through worker-orientated labour legislation and the regulation of product markets and business relationships under consumer-orientated competition legislation. The discussion explores the limitations created by the siloing of regulatory approaches to enabling collective bargaining for workers covered by different statutory regimes. We identify that in both labour and competition laws, meaningful access to collective bargaining in fissured work contexts has been frequently stifled by misplaced assumptions about the nature of the regulatory target and the power distribution in business networks. The article contends that a regulatory response to fissuring (or the problem of ‘the networked firm’) would straddle the labour/competition law divide in various ways, to ensure fissured workers are no longer excluded from exercising collective power by both legal domains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".