The Positive Role of Small Businesses in Scaling down the Shadow Economy Phenomenon and Its Negative Impact on the Private Sector and the National Economy: An Exploratory Study in the Province of Jeddah
Bibliographic record
Abstract
The shadow economy phenomenon is widespread and has detrimental effects on the business sector and the national economy. The philosophy of shadow economy involves complex and overlapping perceptions related to conducting legal as well as illegal businesses. The shadow economy is not a new concept but one that has existed since the first known system of tax levies, and that has spread throughout the developed and developing countries of the world in equal measure. Shadow economy is often associated with illegally-gained proceeds (dirty money) originating from unethical and unlawful small businesses such as sexual slavery/exploitation and the drug trade. It is considered as detrimental to national economies and is rapidly spreading globally in the modern era. Weak, poorly designed or implemented controls by some countries, in addition to administrative corruption, have significantly contributed to the emergence and spread of shadow economies. The expanding scale and growth of shadow economies comes in spite of international efforts to enforce laws and regulations on this issue. The phenomenon has continued to spread despite global efforts to create and increase public awareness on the issue and notwithstanding dissemination of information to increase knowledge of the risks arising from this phenomenon and its economic impacts. Based on the severe threats shadow economies constitute and their effects on economic and social order, the subject has been recently included in the syllabus and course design of international as well as Arab universities. Legitimate and illegitimate business owners and individuals engage in shadow economies, whereby they attempt to evade payment of state taxes by benefiting from widely pervasive administrative corruption.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".