Import Components and Import Multipliers in Australian Economy: World Input-Output Analysis
Bibliographic record
Abstract
This article analysis on import components and import multipliers, using Australian input-output tables 2000, 2005, 2010 and 2014. The results showed that firstly, Australian import components of input were, on average, less than 20 per cent; meaning that input that locally provided were more than 80 per cent. Australian import of input had increased significantly from US$ 47,122 million in 2000 to US$ 14,616 million in 2014. Secondly, Australian imports have been dominated by Sector-8, Sector-13, Sector-24, Sector-25, and Sector-26. Thirdly, Australian imports have been dominated by the USA, Japan, United Kingdom, China and Germany. During 2000-2014, import from Canada, Japan, UK and the USA had declined, but import from China had significantly increased. Finally, highest sectoral import multipliers occurred in Sector-5, Sector-22, Sector-29, Sector-30, Sector-31, and Sector-32, but there was no significant different of import multipliers for country origin of import. Keywords: import components, sectoral import multipliers, spatial import multipliers. JEL Classifications: C67, D57, F17.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".