Economic Growth of Saudi Arabia Between Present and Future According to 2030 Vision
Bibliographic record
Abstract
Saudi Arabia follows a development strategy depending on many factors generating income, such as increasing non-oil investments, production and manufacturing for exports. Investing contributes mainly to diversify sources of income and generate more jobs where it is expected that the contribution of the private sector will enhance productivity in all sectors. These increased business productivities will increase the percentage annual growth rate to 5.2% in addition to increasing the added value of the oil sector. Saudi Arabia implemented a lot of policies to be out of the oil control on their economies and this is taken up in the previous papers of growth factors in Saudi Arabia until 2014. But due to the need of less dependence on oil revenues and the need to diversify sources of income, especially in the period following the drop-in oil prices, it’s necessary to create added value to the economy of Saudi Arabia, through an econometric model that illustrates oil alternatives income. This paper is based on the analysis of different growth factors after exclusion of oil revenues using the Weighted Least Square.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".