Heterogeneous Macroeconomic Effects of Anti-crisis Measures During Global Crises Across Different Spheres of the Economy
Bibliographic record
Abstract
The COVID-19 pandemic spread has proven that a single state, institution or person cannot tackle such intricate and entwined economic, environmental, social and technical problems. The pandemic, in particular, accelerated the necessity of governments to make systemic changes, which have been evident before its start. The time to restore trust in policies and make decisive choices is fast approaching, as the urgency of re-prioritizing and reforming systems continues worldwide. The main directions and peculiarities of the anti-crisis measures of many developed and developing countries and the impact of several crises on the leading macroeconomic indicators of the selected countries have been studied and presented in the paper. A comparative analysis of macroeconomic indicators with the countries that have developed, developing and transition economies, such as the United States of America, Canada, Japan, Australia, China, India, Brazil, neighboring countries of Armenia and EAEU member- states was carried out. All of that allowed us to identify the effectiveness of their implementation mechanisms and evaluate the tangible economic results.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".