<scp>USA–China</scp> trade war: Economic impact on Indonesia
Bibliographic record
Abstract
The trade war between the US and China is one of the big problems that have a propagating effect on other countries. Even though the tension is currently on the decline, the impact on the slowdown in the global economy is expected to continue until 2020. The effect of falling commodity prices that underpin Indonesia's economy is that the Indonesian economy only grows at around 5%. The World Bank estimates that the global slowdown will suppress Indonesia's economic growth next year until 2020. Pressures on the stability of the Rupiah exchange rate continued to occur, especially at the beginning of 2019 and the end of the first semester of 2019. In the second quarter of 2019, fluctuations and depreciation pressures on the Rupiah were recorded quite high. During 2019 Indonesia's trade performance slowed compared to the previous year. Contractions occur in both oil and gas and non‐oil and gas commodities. The weak performance of Indonesia's trade balance is influenced by several factors, including falling demand from Indonesia's central export trading partner countries and also contracting commodity prices on global markets. Another impact of the US–China trade war is the pressure of the Indonesian economy and the decline in primary commodity prices affect investment and import performance. This growth is still relatively good, although slowing compared to the previous quarter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".