Bibliographic record
Abstract
This study aims to determine the determinants of foreign direct investment (FDI) in Indonesia's manufacturing sector. This study uses time-series data with 40 data observations starting from the 1st quarter of 2010 to the 4th quarter of 2020. The data analysis method employed in this research was Autoregressive Distributed Lag (ARDL) cointegration approach. The research results were that in the long run, the exchange rate and GDP growth had a positive effect, inflation had a negative effect, and gross fixed capital formation did not affect the FDI inflows in the manufacturing sector. This research implies that the government must be able to create or develop policies related to foreign direct investment to provide benefits for economic development in Indonesia. The government's efforts to control inflation have to be strengthened continuously by maintaining the availability of supply and distribution of goods. Supply continuity and smooth distribution between regions have to be further improved through the utilization of information technology and the strengthening of inter-regional cooperation. Likewise, efforts to increase economic growth have to continue to be improved by providing incentives or facilities to companies at various levels, both those that are export-oriented and those that focus on domestic sales. Â
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".