How Australia's economy gained momentum because of Covid‐19
Bibliographic record
Abstract
Abstract The objective of the paper is to assess the resilience of the economy of Australia following the Covid‐19 pandemic that hit the global economy in Q4 2019, in years 2020, 2021 and 2022. Quarterly growth rates (annualised) of the Real GDP of Australia and Canada are forecasted between Q2 2022 and Q4 2050. Two sets of forecasts are generated: forecasts using historical data including the pandemic (from Q1 1961 to Q1 2022) and excluding the pandemic (from Q1 1961 to Q3 2019). The computation of the difference of their averages is an indicator of the resilience of the economies during the pandemic, the greater the difference the greater the resilience. Used as a benchmark, Canada's economy shows a slightly lower resilience to the Covid‐19 pandemic (+0.37%) than Australia's economy (+0.39%) based on Q2 2022–2050 forecasts. However, driven by stronger growth than Canada, the average estimate of the Q2 2022–Q4 2050 quarterly (annualised) growth rate forecasts of Australia is expected to be +2.09% with the Q1 1961–Q1 2022 historical data while it should be +1.61% for Canada. Supported by higher growth, Australia's Real GDP is expected to overtake Canada's in Q1 2040.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".