The Role of Small Businesses (Small Scale Economic Projects) in Alleviating the Acuity of Unemployment
Bibliographic record
Abstract
Small businesses are the essential constituents in the development of economy and generating employment opportunities within country. Unemployment is the major concern, which is spreading its roots in various forms and dimensions. The objective of the study was to examine the role of small businesses in alleviating the acuity of unemployment rate. Quantitative approach has been utilized to examine the role of small businesses. Therefore, data was collected from general statistics, directorate of the Kingdom of Saudi Arabia, during the years 2005-2013. Multiple Regression Analysis using the time series approach has been implemented to analyze the gathered information through SPSS version 20.0. The findings have stated that there was a significant impact of small projects on the unemployment rate of Saudi Arabia (p=0.097). Furthermore, statistically insignificant results were revealed for amount of funding variable (p=0.451). A negative relationship has been determined between unemployment rate and amount of funding; while positive and direct relationship was determined between unemployment rate and population. Small businesses are vital in escalating the economy of the country and sustaining the environmental protection. However, small businesses must amend themselves according to the strategies devised for large projects to contribute more to alleviate unemployment rate in the Kingdom of Saudi Arabia.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".