How young people are faring: key indicators 2005: an update about the learning and work situation of young Australians
Bibliographic record
Abstract
This report is the seventh in an annual series documenting changes in the learning and work circumstances of young Australians, though it is noted that this year it is being published in a very different policy environment to previous years. The report suggests that improving the education and training of young Australians is more important than ever because of skills shortages and an ageing workforce. A key finding is that each year a substantial proportion of young Australians make a poor transition from school to further study and work. Around 15 per cent of 15 to 19 year-olds are neither in full-time work nor full-time study. Three out of every 10 young Australians has a precarious or negligible attachment to work one year after leaving school. A quarter of Australians aged 18 to 19 are not in full-time education and work. The situation for 20 to 24 year-olds is similar, and these proportions have been almost unchanged for a decade or so. Reflecting on this latest data, the author notes that this is a problem that seemingly will not go away, even in spite of an unprecedented period of sustained economic growth. Many of these young Australians want to work or want to work more. More than a third of those outside the labour force and not studying full-time want a job. Most of the unemployed are seeking a full-time job, even though the trend over the last decade or longer has been from unemployment to part-time work, and most of those with part-time jobs want more hours of work. While the number of full-time jobs has continued to grow for the older population, it has not grown to the same extent for young Australians outside full-time study. Part-time employment has increasingly and for longer become the basis of their economic survival. As part-time work becomes a way of life for more young Australians, it may provide an extended stepping stone to full-time work. Equally, however, it may provide a poor foundation for future skill development or full-time engagement with the labour force. Australia has one of the highest levels of non-student part-time employment among OECD countries. Governments at all levels have initiated policies and programs designed to improve the transition of young people from school to further study and work, but many of the initiatives are relatively recent and their effects may not be seen for some time yet. Indigenous youth are possibly the most disadvantaged group in terms of education and employment. They are significantly under-represented both in universities and in New Apprenticeships, although their participation in the vocational education and training (VET) sector as a whole is better.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".