SMEs in Sultanate of Oman: Meeting the Development Challenges
Bibliographic record
Abstract
The small and medium enterprises (SMEs) not only play an important role in the economy of a country but are crucial to the country’s economic stability. In most countries SMEs generate a substantial share of GDP and a key source of new jobs as well as breeding ground for entrepreneurship and new business ideas. The United States of America, UK, Japan, Australia, Newzland, Canada and other developed, as well as developing countries are making policies to facilitate the growth of SMEs. In New Zealand SMEs makeup more than 99% of all business and account for about 60% of employment. In the USA more than half of all the employment comes from firms with fewer than 500 employees (Baldwin et al 2001 ). In the UK, SMEs employ 67% of the workforce (Lange et al 2000 ). Estimates from the World Bank indicates that SMEs have contributed over 55% of GDP in OECD countries and between 60 to 70 per cent of GDP in middle income and low income countries generating 60 to 70 percent employment ( Oman Economic Review, 2007 ). The above facts show that SMEs play a very important role in the growth of economy of a country, and Oman is not an exception.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".