The Impact and Contribution of FDI to Saudi Economy During King Abdullah Regime
Bibliographic record
Abstract
Foreign Direct Investment resulted in the disclosure of different investment chances and opportunities through active investment promotion agencies. A country must execute various reforms capable of improving the fundamental determinants of FDI for achieving a high percentage of Foreign Direct Investment. These reforms among others include improving investment laws, reducing political risk and level of corruption, establishing a consistent legitimate and regulatory environment, freeing repatriation of funds and capital, as well as opening up to international trade. Saudi Arabia adopted generous incentive policies for attracting foreign capital and invite Foreign Direct Investment during king Abdullah regime. These policies present positive incentives while eliminating negative disincentives. Positive incentives consist free custom duties, reductions of tax and export zones, by the government of Saudi Arabia. Disincentives elimination to investments indicates the removal of overlong and rigid systems as they can delay visas issuance, restraint travel and complicate the licensing and registration of a project. This paper discusses the impact of FDI on Saudi economy during King Abdullah regime and finally, ascertains the contribution of FDI to Saudi Economy during King Abdullah regime.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".