The Role of Government Expenditure on Regional Economic Resilience During Pandemic Covid-19
Bibliographic record
Abstract
The GDP growth rate in the second quarter of 2020 YoY decreased by 5.32%. This contraction was triggered by the COVID-19 pandemic which had an impact on economic activities. The policy of restricting the mobility of people and the closure of various business sectors has resulted in social and economic problems. This study examines government fiscal instruments to maintain the sustainability of the public economy. The analysis used in this study is a mixed method. Empirical testing is done by using regression techniques. The research variables include regional economic performance, spending on the prevention of COVID-19, spending on social safety nets, and spending on handling economic impacts. The research is also based on a questionnaire conducted with local governments regarding health performance and economic recovery efforts. The data used is a cross-section of the period at the end of the second quarter of 2020. The test results prove that the budget reallocation used for spending on COVID-19 disease control and social safety net spending affects improving the regional economy during the study period with p-values of 0.021 and 0.000. The government, through the Sub-National Budget, continues to refocus spending for economic recovery.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".