Small and Medium Entrepreneurship in Saudi Arabia's Vision 2030 and Its Role in Reducing Unemployment in the Kingdom of Saudi Arabia
Bibliographic record
Abstract
SMEs (small and medium-sized enterprises) account for 60 to 70 percent of jobs in most OECD (Organization for Economic Cooperation and Development) countries with a particularly large share in Italy and Japan, and a relatively smaller share in the United States. Throughout; they also account for a disproportionately large share of new jobs, especially in those countries which have displayed a strong employment record.In Saudi Arabia, more than two-thirds of the population are younger than 30 and about 100,000 graduates enter the job market each year. The present paper discusses what measures should be taken and highlights the importance of understanding the relationship and interaction between unemployment and entrepreneurship. The present research focuses on studies that explain small and medium-sized entrepreneurship and its role in reducing unemployment in the Kingdom of Saudi Arabia. Both qualitative and quantitative approaches are used to collect data that may contribute to a better understanding of the employment issues of Saudi young men and women. Besides; we introduce entrepreneurship Survey Questionnaire; this survey has focused mainly on understanding the relationship and interaction between unemployment and entrepreneurship. In this survey, a more balanced view is taken by examining the factors which contribute to creating more job opportunities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".