Bargaining over Australian public service cuts: Do forcing strategies work?
Bibliographic record
Abstract
Abstract Although a Labor government fiscal stimulus had helped Australia weather the 2008 global financial crisis, budget deficits increased, and the public service was targeted for economies. The Liberal/National (Coalition) opposition won office in 2013, promising public sector cuts. In this context, the Walton et al. concept of a forcing strategy helps analyse the 2014–2016 bargaining round in the Australian Public Service. A forcing strategy involves three negotiating processes: distributive bargaining to achieve concessions in pay and working conditions, the structuring of attitudes to heighten animosity between the negotiating parties, and the management of internal differences to minimise intragroup conflicts. The Liberal/National (Coalition) government adopted elements of these approaches, requiring Australian Public Service agencies to reduce a range of employment conditions to justify pay increases. Interactions between Australian Public Service management and the principal Australian Public Service trade union, the Community and Public Sector Union became increasingly hostile over the course of the bargaining round. In addition, internal differences emerged between the Australian Public Service Commission, which oversaw the bargaining process, and individual Australian Public Service agencies. We consider the efficacy of this forcing strategy in light of the potential for the Community and Public Sector Union to mobilise its membership to resist such an approach to pay negotiations.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".