Australia's gender pay equity legislation: how new, how different, what prospects?
Bibliographic record
Abstract
Australia’s equal pay laws have recently been renovated through the Workplace Gender Equality Act 2012 and the Fair Work Act 2009. In light of these changes, it is timely to ask how effective Australia’s legislative approach is likely to be for progressing pay equity. This article presents an analysis of Australia’s current equal pay provisions, assessing their potential on the basis of their operation to date and through recent experience in Canada and the UK. Although focused on outcomes, we argue that Australia’s new workplace-based mechanism under the Workplace Gender Equality Act may prove relatively ineffective in both diagnosing and remedying pay inequality. In comparative perspective the Fair Work Act provisions provide significant capacity to improve pay equity across large sectors of the labour market. To date the use of these provisions point to some practical limitations in realising this potential. Moreover, the inadequate legislative and policy integration between labour market, sectoral, workplace and individual approaches together with a wavering political commitment to equality legislation generally suggest gender pay inequity will remain a persistent feature of Australian employment.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 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".