Indonesian Government Policy in Mitigating Economic Risks due to the Impact of the Covid-19 Outbreak
Bibliographic record
Abstract
Covid-19 pandemic have a negative impact on economies globally, including in Indonesia. The disease is advancing at great speed since the first Indonesian patient was referred to the hospital due to confirmed covid-19 (26 February 2020) until on 15 June 2019 there have been 50,187 patients infected. Several government policies have been implemented by regarding the economic sector as a main concern to prevent the breaking of the Indonesian economic chain. To anticipate, March 31, 2020 Indonesian President signed Government Regulation No. 21 of 2020, which regulates the implementation of PSBB (Large-Scale Social Restrictions), yet economic growth in the first quarter of 2020 showed a declining performance at 2.97 percent on 17 April 2020. Bank Indonesia views the level of the Rupiah exchange rate as fundamentally "undervalued". The objective of this paper is, therefore, to overview the negative impact of the covid-19 outbreak on the Indonesian economy and the policies implemented by the government to mitigate the economic risks. Moreover this article is a normative economic analysis on the basis of secondary data, this study found that Indonesia is facing up an economic domino effect of covid-19 and Bank of Indonesia (BI) has taken several steps by strengthens policy coordination with the government and other authorities to stabilize the rupiah exchange rate and mitigate the impact of Covid-19 risk on the domestic economy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".