Productivity Commissions: the new public policy tool of global competitiveness? The Argentina-Australia case.
Bibliographic record
Abstract
The comparative analysis of long-term developments in Argentina and Australia is a historic issue in the academic fields. This may be due to the fact that both countries belong to the group of the so-called fortunate countries, for their availability of vast territorial areas (Australia with 7.7 million km 2 and Argentina with 2.8 million km 2 continental areas), low population rates (only about 24 million inhabitants in Australia and over 43 million in Argentina) and significant natural, agricultural and mineral resources. Brazil, China, the United States, Russia, India, Canada, the Democratic Republic of the Congo and even Indonesia are also large countries with immense natural resources. However, when considering the present value and the future potential of natural resources per capita, Argentina and Australia, together with Canada, clearly emerge as global leaders in the global context. Both countries are, geopolitically speaking, located in the so-called ends of the world, but currently, Australia, close to Southeast Asia, is heavily influenced by China economic dynamism. Moreover, both countries are the result of European colonization but by different kingdoms. Argentina was colonized by Spain in the mid-16th century while Australia was populated since the end of the 18th century by convicts sent by the British government (to relieve further overcrowding of British prisons), along with English, Scottish and Irish settlers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".