Reflections on the Economy of ASEAN countries in the face of the Covid-19 Pandemic
Bibliographic record
Abstract
This article discusses the Economic Reflections of Asean countries in facing the Covid-19 Pandemic in several Asean countries, namely Vietnam, Malaysia and Indonesia. Vietnam's economic growth was victorious, the economies of various countries in other Southeast Asian regions were battered by the corona virus. The process of economic growth is influenced by two kinds of factors, namely economic factors and non-economic factors. Economic factors, which are none other than production factors, are the main force affecting economic growth. Malaysia has proven to the world community that its country is capable of managing its economy even in challenging circumstances. He quoted the IMF as global economy recorded negative growth and in Indonesia it seems that contraction in income activities in some income classes is affected. In the second quarter there is a slowdown, then in the third quarter the savings are enormous. It could be that consumption, which has been a factor in economic growth, will be a challenge. In an effort to maintain economic stability during the Covid-19 pandemic. This reflects that the economies of ASEAN countries, even in the world, are currently under the same pressure due to the Covid-19 virus pandemic, the world economy this year will experience a 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".