Legal Aspect to Ensuring Sustainable Development in Jordan: Countering Crime and Shadow Operations
Bibliographic record
Abstract
In Jordan, addressing crimes such as corruption, smuggling, and unregulated economic activities through robust legal measures is crucial.These activities can significantly hinder sustainable development by diverting resources, harming the environment, and fostering inequality.In such conditions, the goal is to develop an approach that could assess the influence factors of shadow business on the sustainable development of Jordan.For this, the scientific task should include determining what is the possible connection between sustainable development in Jordan and security in the country.The system of sustainable development of the economic system of Jordan was chosen as the object of study.For this, the methodology involves the application of the method of correlations, the method of variance analysis and the method of nonlinear analysis.PESTLE and SWOT analyzes of the impact of shadow business operations in the region on ensuring sustainable development were conducted.All complex calculations were carried out using special software for working with data analysis.As a result, a detailed analysis was carried out and substantiated conclusions were formulated on the dynamics of the corruption perception index in Jordan.An assessment is made of the state of the country's economic security over the past five years and its impact on sustainable development.The study found that economic crimes and criminal offenses have a significant impact on sustainable development in Jordan.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".