Bibliographic record
Abstract
After a prosperous youth starting from the early 1900s, Saudi Arabia is finally starting to face some of its first real challenges of the 21st century in the form of high rate of unemployment. This paper seeks to determine the root causes of the persistent rise in unemployment in Saudi Arabia. In addition to more general causes, it also looks at the historical foundations of the problem of unemployment in the nation. The paper explains the high economic and social costs of unemployment and also determines the empirical relationship between unemployment and loss in Gross Domestic Product (GDP), utilizing Okun’s law and applying recently developed panel econometrics techniques. Additional details about the social costs of unemployment are also explained. The primary goal of the paper is to develop an approach to estimate the cost of unemployment in Saudi Arabia more accurately. This paper utilizes alternative approaches such as the average product method as a failsafe to double-check in situations where Okun’s law could not be applied. Thus, this paper will detail the potential risks that threaten the nation as an effect of unemployment. Finally, the main findings of this paper is that the loss of the total real GDP is $ 95 billion, while the loss of the non-oil real GDP is $ 95 billion as a result of 1,687,313 Saudis unemployed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".