Police Bargaining Disputes and Third-Party Intervention in Australia: Which Way Forward?
Bibliographic record
Abstract
The essential duties that police officers perform, and the absence of a right to strike, creates the need for an effective, impartial procedure for the resolution of bargaining disputes. This article argues that, with the shift of focus under the Fair Work Act 2009 (Cth) to good-faith bargaining, police officers have been left without an effective dispute resolution mechanism, partly because of the limitations on arbitration but also because of uncertainties surrounding the scope of the ‘protected action’ provisions of the Act for police officers. Following a review of police pay-setting arrangements in comparable jurisdictions, this article examines and proposes options for an alternative model, including a mandatory ‘final-offer’ arbitration (‘FOA’) model as used for police bargaining in Canada, New Zealand and the United States. Research shows that — aside from providing an effective closure mechanism for bargaining disputes where strikes or lock-outs are unavailable — mandatory FOA offers a range of benefits to police bargaining, and could provide an ideal ‘fit’ for the current bargaining-centred system. The article’s findings are of significance not only to police officers, but to all emergency services workers covered by the Fair Work bargaining regime.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".