Kajian Kerentanan Ekonomi Indonesia terhadap Pandemi COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic is a serious problem for the economies of many countries, including Indonesia. Low specimen testing capacity, causing uncontrolled transmission. The Indonesian economy is faced with a recession. The economic vulnerability to the COVID-19 pandemic needs attention as a basis for making the right policies. This study aims to build an economic vulnerability index to COVID-19 and map the vulnerability of the regional economy to form priority groups for economic policies. This index consists of two dimensions: exposure and shock. It was found that the score for Indonesia’s economic vulnerability index to COVID-19 reached 56,58. Provinces in Java Island tend to have high economic vulnerability, especially DKI Jakarta. Furthermore, the economic vulnerability index has a significant negative relationship with the GRDP growth in the 2nd quarter of 2020. Through quadrant analysis, four priority groups were obtained. Priority I consist of DKI Jakarta, Banten, West Java, Bali and DI Yogyakarta which need more attention because of high possibility of shocks and structurally more exposed to the economic impacts caused by the COVID-19 pandemic shocks.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".