Trade balance during the Covid-19 pandemic
Bibliographic record
Abstract
Abstract This paper discusses Indonesia’s trade balance during the first quarter of 2020 and examines developments in important sectors. The study investigates what policies need to be taken to make Indonesian economy survive. Based on quantitative and qualitative data, Indonesia’s trade balance in April 2020 experienced a deficit of 344.7 million US dollars in the export-import performance of the oil and gas and non-oil and gas sectors, after a surplus of 715.7 million US dollars in the previous month. This slump was due to the decline in export performance of manufactured products and mineral fuels, which was influenced by slowing demand, disruption of global supply chains, and low commodity prices in line with the impact of the Covid-19 pandemic. However, positive performance of exports in gold, iron, and steel and vegetable oils and fats were able to prevent further decline in non-oil and gas. Despite experiencing a deficit in April, Indonesia’s trade balance from January to April 2020 remained surplus of 2.25 billion US dollars. To prevent the trade balance decreasing, government’s policy need to strive for independence and sovereignty in fulfilling logistics in the context of national resilience by strengthening the industry and domestic production capacity in various vital sectors.
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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 | 0.000 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".