Bibliographic record
Abstract
Australia has historically been Canada's poorer cousin. But a pick-up in productivity growth in the 1990s has raised Australian living standards to Canadian levels. In this article, Dean Parham of the Australian Productivity Commission provides an overview of Australian economic performance and the policy reforms that turned around Australia's laggard productivity growth. He first points out that during the first half of the 20th century Australia enjoyed one of the highest levels of labour productivity in the world. But Australia never experienced productivity convergence in the postwar period up to the 1990 and saw its productivity and GDP per capita ranking decline over this period. Productivity growth then picked up in the 1990s, with output per hour advancing 2.3 per cent per year in 1990-2001 compared to 1.5 per cent in 1973-1990. It was increased multifactor productivity growth, not capital deepening, that drove this acceleration. Parham makes the case that policy reforms explain much of Australia's improved productivity performance. He identifies three broad areas of policy reform as particularly important in fostering productivity growth: sharper competition; greater openness to trade, investment and technology; and greater flexibility for businesses to adjust production and distribution processes. These reforms spurred the Australian economy to to embark upon a much delayed productivity catch-up.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".