Bibliographic record
Abstract
The attainment of ‘fairness’ is widely regarded as a worthy goal of setting minimum wages, but opinions differ sharply over how to achieve it. This article examines how interpretations of fairness shaped the minimum wage decisions of the Australian Industrial Relations Commission between 1997 and 2005. It explores the Commission's approaches to three aspects of fairness in minimum wages: first, eligibility for increases; second, the form of increase; and third, the rate of increase over time. The Australian Industrial Relations Commission consistently gave minimum wage increases that were expressed in dollar values and applied to all federal awards. Its decisions delivered real wage increases for the lowest paid, but led to falls in real and relative wages for the majority of award-reliant workers. Fair Work Australia, the authority now responsible for setting minimum wages in the national system, appears apprehensive about parts of the Australian Industrial Relations Commission's legacy and has foreshadowed a different approach, particularly with respect to the form of adjustment.
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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 | 0.047 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".