Smart Australians: Education and Innovation in Australians
Bibliographic record
Abstract
Australians go the extra mile to make sure their children receive the best possible education which is now one of the top 15 expenditure items for Australian families.Over recent years, family spending on preschool and primary education has increased by 79 per cent and spending on secondary education has increased even more by 101 per cent.Public and private investment in education is paying off as we raise a generation of children that are more likely to stay in school and more likely to go to university.Education levels are on the rise with Australians spending an average of 12 years in school.Participation in tertiary education has jumped substantially with more than 44 per cent of 25-34 year olds having a tertiary education, compared to about 30 per cent of 55-64 year olds.One of the founding fathers of the United States, Benjamin Franklin, said it best when he stated that: "An investment in education pays the best interest".New arrivals to Australia are further bolstering our education levels.Australia has a highly educated migrant population and younger migrants in particular are far more likely to hold a Bachelor Degree or above than those born in Australia.A better educated population leads to more innovation.The Smart Australians report shows Australia punches well above its weight in this endeavour, being granted more than its share of patents at 10 per cent of the world's total and making significant increases in research and development funding.We have also seen a strong upward trend in the number of local trademark applications.It is this innovation that drives our economic growth and marks our nation's progress.Australia's competitive advantage is its lifestyle.A well-resourced education system and a culture of innovation are crucial components of this enviable lifestyle and investing in both will help ensure our nation is indeed a truly lucky country.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".