Pengaruh Saudi Vision 2030 dan Agenda Foreign Direct Investment(fdi) Arab Saudi di Indonesia
Bibliographic record
Abstract
Saudi Arabia is a rich country whose source of income is almost 90% comes from oil and gas. However, since December 2014 world oil prices plummeted to US $ 40 per barrel, previously had felt the world oil price above US $ 100 per barrel. In addition to the phenomenon of the world oil price drop caused by rising production of US Shale oil, the constellation of politics in the Middle East continues to heat up also trigger Saudi Arabia to reform its economy for Saudi Arabia off its dependence with oil by diversifying its economy and become a middle power country in the Middle East region And Arab countries. The Saudi Arabian Reform effort is contained in Saudi Vision 2030.Indonesia is one of Saudi Arabia's vital partners in realizing Saudi Vision 2030. The Saudi Arabian focus on economics in Saudi Vision 2030 is the Foreign Direct Investment Agenda (FDI). In analyzing the influence of Saudi Vision 2030 on the Saudi Foreign Direct Investment Agenda in Indonesia, this research uses a perspective of liberalism supported by the concept of the nation state and FDI theory. Saudi Vision 2030 has a positive influence on the increase of Saudi Arabian cooperation in the field of economy especially in the field of Investment, as seen from the visit of King Salman to Indonesia, the signing of 11 MoUs, Realization of Saudi Arabia Investment in the first quarter of 2017 showed a positive increase and optimistic will continue to rise Which is significant and also the investment policy that is constantly updated by both countries to facilitate each other and give comfort to invest.
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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 | 0.000 |
| 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) | 0.015 | 0.005 |
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".