Bibliographic record
Abstract
This study aims to determine the factors that influence manufacturing exports inIndonesia. This study uses time-series data with 40 data observations starting fromthe 1st quarter of 2010 to the 4th quarter of 2019. This study's analysis method is the vector error correction model (VECM), which can dynamically describe the shortterm and long-term effects. Export determinants to be examined are inflation, the rupiah exchange rate, Gross Domestic Product (GDP), and Foreign DirectInvestment (FDI). This study indicates that inflation at lag 1 harms manufacturedexports both in the short and long term. Furthermore, GDP has a positive effect onmanufacturing exports in the short run at lag 1 and lag 2, while in the long run, GDPhas a positive effect only on lag 1. Meanwhile, the exchange rate and FDI factors didnot affect manufactured exports, both in the short and long term. This study impliesthat inflation and GDP are essential factors in designing policies to increase exportsin Indonesia, including exports of manufactured products.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".