Bibliographic record
Abstract
The objective of this paper concentrates on determining the relationship between unemployment and economic growth in Saudi Arabia for the period 2000-2015 in order to explanation of the employment, unemployment level and its determinants to increase the employment level and avoiding the harmful effects of unemployment problems. The question to be raised is does recruitment rely on the public sector? Does the creation of job opportunities in the state’s public sector have a negative or positive effect on the private sector through the effect of withdrawing its specialized technical cadres? Is the private sector growth real or illusive? Is the economic growth adequate to reduce the unemployment rate among Saudis? The results obtained show that, there are a positive relationships between the employment and real income, real investment, real government expenditure and real value of exports. On the other hand, there are negative relationships between employment and the real value of imports. The economic growth was not adequate in reducing the unemployment rate among Saudis. There is a reversal relationship between unemployment rates and the economic growth which does not effectively work in the Saudi economy. Saudis prefer to work with government sector not in private sector; Government must stimulate Saudis to work in private sector. This paper used the annual data from 2000 to 2015 for Saudi Arabia. All data in this paper was obtained from Saudi Arabian Monetary Agency (SAMA) and World Bank Development Indicator.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".