Effect of COVID-19 Stimulus Packages on Nations’ Competitive Advantage
Bibliographic record
Abstract
The study examines the country’s competitive advantage variations due to fiscal stimulus allocated for COVID-19 by the G-20 governments. It predicts that G-20 countries that are more likely to attract future investments from global firms will improve their trade share in the post-COVID-19 scenario. The study uses the growth-share matrix and 4E (entrenching, empowering, enterprising, enriching) framework. Findings indicate that Japan, the USA, India, Australia, and Canada have allocated significantly large stimulus as a percentage of gross domestic product (GDP) compared to their world trade share. It is likely to provide them with a competitive advantage in the future. The findings further reveal that the Governments have significantly allocated the stimulus to four sectors, that is, health, social security, industry and construction, and small and medium enterprises (SMEs). In the post-COVID-19 scenario, global firms may seek market entry or expansion strategies in these sectors in the nations mentioned above.
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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 | 0.003 | 0.017 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".