Bibliographic record
Abstract
Over the next eight years, employment in Australia will grow to almost 14 million jobs, a net increase of some 1.6 million jobs. In which industries and regions will these jobs be? What occupations will the workers perform? The labour market in Australia is constantly changing. It is unlikely that these questions will have the same answers in 2025 that they have today. The Victoria University Employment Forecasting (VUEF) project attempts to address these questions, in the context of a macroeconomic model that has the capacity to incorporate detailed structural and demographic change. As a generation of baby-boomers retires and a new generation – many with degree-level qualifications in management and commerce, society and culture, health and other fields – enters the workforce, the service industries will continue to dominate. The modelling finds that just three industry divisions – health care and social assistance, professional services, and education and training – will account for more than half of employment growth over the next eight years. Accordingly, employment in the professional occupations will continue to grow strongly, adding almost 600, 000 jobs to employ 3.4 million people, or a quarter of the workforce, by 2025. A gradual reversal of some of the adverse conditions affecting employment in the manufacturing and agricultural sectors will see a return to positive, albeit modest, growth rates in these sectors. High urban population growth forecasts and the dominance of growth in the service industries mean that more than 75 per cent of employment growth, or a net increase of 1.2 million jobs, will be in the capital cities. Melbourne and Sydney will account for just over half of the forecast growth in national employment. Full or partial subscriptions to the 2017 edition of the detailed VUEF database are now available from the Centre of Policy Studies at Victoria University.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.101 | 0.148 |
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".