Old game, new rules? The dynamics of enterprise bargaining under the <i>Fair Work Act</i>
Bibliographic record
Abstract
Over the last quarter century, enterprise bargaining has evolved to be a primary mechanism through which wages and conditions of employment are determined in Australia. Since the introduction of the Fair Work Act, the process for negotiating enterprise agreements has been governed by principles of good faith bargaining. There has been considerable debate over the potential for these provisions to change the dynamics of bargaining, yet empirical evidence of these effects remains limited. This article reports on a field study investigating the experiences of industrial parties negotiating enterprise agreements during the first three years of the Fair Work Act. Drawing on the tribunal's own case management database, along with a large sample of interviews, the study provides a more systematic examination of the extent to which the parties have deployed the new principles governing collective bargaining, with a particular focus on good faith provisions, and whether these principles have altered the dynamics of bargaining practices.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.036 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".