Bibliographic record
Abstract
The centralised establishment of minimum wages and the role of awards in determining the wages of employees across an industry or occupation have long been central features of Australia’s wage-fixing system. One key change has been the greater prominence given to bargaining (most recently under the Fair Work Act 2009 (Cth) (Fair Work Act)) at an enterprise level. The federal tribunal responsible for the role of national wage-fixing has undergone a number of major changes including the rationalisation and modernisation of awards to apply nationally, that is, across former federal and state jurisdictions. In the Annual Wage Review 2009–10,1 the then Minimum Wage Panel noted the need for research into the composition of the award-reliant workforce. The Minimum Wage Panel recognised that an understanding of award reliance is essential to the minimum-wage setting process, stating in its decision for the Annual Wage Review 2009–102 that to inform future reviews it was seeking research to explore the extent and composition of the award-reliant sector. This project on award reliance was conducted to examine these issues. It was undertaken by the Workplace Research Centre, University of Sydney Business School (WRC) in collaboration with fieldwork company ORC International (ORC) on behalf of the Fair Work Commission. It was supplemented by two other projects, one of which considered incentives to enterprise bargaining among a range of industries in Australia, and the other being a qualitative study of professionals and other employees on higher award classifications. The focus of the Award Reliance Survey was to \nquantitatively investigate award reliance across and within Australian organisations, and to identify the mix or ‘categories’ of award-reliant employees and their location on award classification scales. The project had two main objectives. The first was to identify the incidence of award reliance across all non-public sector organisations and employees at the organisational level. The second was to identify the nature of award reliance across all non-public sector award-reliant organisations (i.e. \norganisations paying at least one employee exactly the award rate) in order to: identify award-reliant employees, and professional and other award-reliant employees on higher award classifications (including the characteristics of these employees); investigate explanatory variables for award reliance in professional and other higher classification award-reliant employees; and explore explanatory variables for award reliance at the organisation level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.031 |
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; both teacher heads agree on what is shown here.
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".