Exploring the Performance of Australian Construction Industry in a Recent Global Recession
Bibliographic record
Abstract
Available data on the recent global financial crisis (GFC) show that it lasted between the second quarter (Q2) of 2007 and the fourth quarter (Q4) of 2009. Australia is one of the first economies to fully recover from this crisis. This study explorles the role played by the Australian construction industry in stimulating economic growth during the recession. In order to investigate the macro-variability trend during the financial crisis, data were collected and analysed relating to the quarterly GDP of Australia and selected countries between Q1 2000 and Q4 2009. Specifically, changes in the construction industry's GDP were compared with aggregate GDP changes in Australian economy and similar indices in the 'Group of 7' (G7) countries and Organisation for Economic Co-operation and Development (OECD) countries. Moreover, specific attention was focused on Germany, France, Japan, United States of America (USA) and United Kingdom (UK). Graphical and Pearson's correlation methods were used to analyse the relationships between changes in construction GDP and Australia's overall economic growth during the recession. In addition, an attempt was made to develop a regression model for predicting economic growth during the recent recession using changes in gross fixed capital formation (GFCF), changes in construction GDP and the impact of these changes on national economy. Analysis shows a slight contraction in construction activities during the crisis; however construction triggered significant growth in the economy during the crisis period and afterwards. This appears to be the major difference between Australia and other major economies that have experienced a longer recession.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".