ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI FOREIGN DIRECT INVESTMENT NEGARA-NEGARA PENDIRI APEC
Bibliographic record
Abstract
Foreign Direct Investment (FDI) is one of the important sources of capital for a country in addition to revenue from the tax sector. The contribution of FDI is quite large in funding development that occurs in asset transfer, technology transfer and management transfer. In reducing trade and investment barriers, then between countries forming regional integration, one of which is APEC where in 2014 half of the world's FDI went to this organization. This study explains the influence of GDP, trade openness, labor force and infrastructure on foreign direct investment of the 12 APEC founding countries, namely Australia, New Zealand, Canada, United States of America, Indonesia, Malaysia, Brunei Darussalam, Singapore, Thailand, Philippines. , Japan, and South Korea. The research method used in this study is multiple linear regression analysis with panel data from 12 countries in the 2009-2018 period. The best model used is the fixed effect model (FEM). The results of the study with eviews 9 show that all variables simultaneously have a significant effect on foreign direct investment in the founding countries of APEC. Partially, the GDP variable has a significant positive effect, the trade openness variable has a significant but negative direction, while the labor force and infrastructure variables have no significant effect on foreign direct investment in the founding countries of APEC
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".