DAMPAK PANDEMI COVID-19 TERHADAP PERTUMBUHAN EKONOMI DI PULAU JAWA
Bibliographic record
Abstract
The Covid-19 pandemic that has hit the world has changed the order of various aspects of life, including Indonesia. Starting from the health, social and economic sectors that were most significantly affected. The economic sector is experiencing recession both at the global and national levels. The island of Java, as the largest contributor in driving the national economic growth rate, cannot be separated from this problem. This study aims to determine how the impact of the Covid-19 pandemic on economic growth in Java Island. This study uses a qualitative descriptive method with a review of various literatures. The results of this study indicate that the economic growth in Java Island which is the most in contraction is Banten Province, namely minus 3.38% and the fastest improving is the Special Region of Yogyakarta Province with the economic growth rate in the fourth quarter of minus 0.68%. To accelerate economic recovery in Indonesia, it must be started from the island of Java because as the largest contributor, namely with the government's policy efforts to revitalize the processing industry, increase access and capital to MSMEs and optimize the use of village funds in alternative development innovations during a labor-intensive pandemic, the development of BUMDes. or developing the potential of a tourist village.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".