Bibliographic record
Abstract
Starting in December 2019, COVID-19 had been spreading across the world on a limited scale for a quarter until March 2020 when the death toll in the countries comprising the Association of Southeast Asian Nations (ASEAN) finally started to mount. However, despite the relatively late outbreak in the region, the ASEAN market had already plunged along with other regional markets across the world amid heightened concern about the economic impact of the biggest viral killer in 2020. In this paper, we examine the economic impact of coronavirus on different ASEAN countries separately by analysing their respective economic figures for the first two quarters in 2020. This allows us to delineate the overall picture of its impact on ASEAN countries as well as to provide an estimation for the future outlook of the ASEAN economic bloc. We propose that the slowing growth, the sluggish recovery of trade and the cross-country transmission of unemployment are three significant risk factors that the ASEAN economies are faced with.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".