ICT 1 The Role of ICT in Australia’s Economic Performance*
Bibliographic record
Abstract
Considerable uncertainty hangs over the world economy at present, from which the Australian economy is not immune. While the future must remain uncertain, what does seem clear is that Australia’s growth performance over the past decade has been exceptional. By the September quarter last year, Australia had notched up nine years of growth averaging 4 per cent annually. This included a dozen consecutive quarters of through-the-year growth of above 4 per cent – the longest run of such growth since the quarterly National Accounts commenced in 1959. In the same decade, the average incomes of Australians rose by 2.5 per cent a year, one percentage point above the previous trend. This growth performance was all the more remarkable for having withstood the financial crises which gripped our major Asian markets – an achievement for which it is hard to find parallels in our previous history, or that of many other countries. Indeed, Australia stands out as one of only a few countries to have significantly improved its growth performance in the turbulent 1990s. Another such country was of course the United States. In both cases, a major proximate source of higher growth was a surge in productivity (the ability of industries to get a bigger output payoff from the physical and human resources available to them). The productivity acceleration in Australia started earlier and, by the Commission’s reckoning, was much more pronounced that that of the United States. However, the US uplift has generally attracted more attention, partly due to the importance of the US economy, but also because it appeared unexpectedly – at a stage in the business cycle when a slowdown in productivity growth would normally have occurred. It
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".