DAMPAK PANDEMI COVID-19 TERHADAP PERTUMBUHAN EKONOMI DI PULAU JAWA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it