Peran Perdagangan International Dan Investasi Terhadap Pertumbuhan Perekonomian Indonesia
Bibliographic record
Abstract
In the context of a country's economy, one of the things that stands out is economic growth. To grow a country's economy, that is by carrying out international trade for a country, such as exports and imports, and making investments. In the context of Indonesia, international trade has become an instrument to encourage economic growth. Meanwhile, investment can increase people's real income, provide greater economic resources for investment (investible surplus), and encourage the rate of economic growth. The type of research carried out is qualitative descriptive research. The data sources used are secondary data originating from journals, books and websites and the data collection technique used is literature study. Based on data from the Central Statistics Agency (BPS), economic growth was below 5% or 4.94% on an annual basis in the third quarter of 2023, this figure is lower than the previous quarter of 5.17% (yoy). This weakness was caused by the decline in export value and imports in the third quarter of 2023. Meanwhile, the role of investment greatly influences economic growth. This result is in accordance with existing theory, where higher investment in a country will increase the country's economic growth. If a country's investment is high, it will contribute to increasing a country's income.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".