Dampak Pandemi Covid-19 terhadap Pertumbuhan Ekonomi dan Perdagangan Komoditas Pertanian di Indonesia
Bibliographic record
Abstract
This study aims to analyze economic growth in Indonesia during the Covid-19 pandemic by using the growth of Gross Domestic Product (GDP) with a comparison of the previous year in the same quarter (y-on-y) and also a comparison with the previous quarter (q-to-y). q). The second objective of this research is to analyze price disparities, price fluctuations, and the trade balance of agricultural commodities. The method used in this research is descriptive analysis. The results of this study explain that economic growth in Indonesia during the Covid-19 pandemic has decreased, starting from the second quarter of 2020 to the first quarter of 2021. Meanwhile, the impact of the Covid-19 pandemic in the agricultural commodity trading sector, namely the existence of a high price disparity reaching above 50% in several commodities such as chicken meat, red chili, beef, and shallots. However, there is price stability for rice, chicken eggs, cooking oil, and sugar commodities. In the trade balance during the Covid-19 pandemic, there was a deficit of 14 thousand tons for beef/buffalo commodities in the January-May 2021 period, while other staple food commodities experienced a surplus. To overcome the problems of trade and economic growth in the agricultural sector, the government should integrate the main market network, improve stock management and logistics, and increase production
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".