Book review: Ian W. McLean. Why Australia prospered: The shifting sources of economic growth
Bibliographic record
Abstract
In a series of articles written over many years, Ian W. McLean has addressed the dual questions of how Australia attained high levels of prosperity less than a century after European settlement and why it has since remained amongst the wealthiest of nations. Although this book is not a comprehensive study of Australian economic history, it builds on this earlier body of work and brings together his answers to these questions. It is engagingly written, helped by the minimal use of technical material and the creation of counterfactual scenarios in several places. Most important of all is McLean's impressive use of the comparative approach. While arguing that Australia's path of development has been strongly shaped by international influences-immigration, investment, trade, and political institutions- he interrogates closely its performance relative to that of other specific nations to tease out national differences as well. These are appropriately selected in most cases: the role of differences in land ownership patterns and political institutions with Argentina, or the greater connection of Canada's timber and grain industries to manufacturing than Australia's wool and mining. However, New Zealand might have featured more strongly in the comparative story.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.025 |
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".