Organized Smuggling of Goods in the Criminal Law of Iran and Turkey
Bibliographic record
Abstract
Smuggling of goods, known as one of the most obvious instances of economic crimes, has done irreversible harms to the economic systems of countries through placing obstacles in the way of productive investment, undermining healthy competitions in business, and finally forming and expanding underground and hidden economies. Creating the areas of money laundering and committing transnationally organized crimes, smuggling jeopardizes the economic and political security of countries seriously. On the other hand, committing smuggling crimes in groups has led to the expansion and intensification of such actions and made it quite difficult for the criminal justice systems to identify and deal with them. Organized crimes, which are among more evolved forms of group crimes, influence different areas of society because such crimes are compulsorily accompanied by the prevalence of bureaucratic and financial corruption. In addition, such crimes have negative impacts on the cultures of societies. Based on the proportionality of crimes, punishments, and distributive justice and the theory of punishment, criminal policy makers, therefore, have considered the quality of committing smuggling crime, such as organization, in order to effectively deal with this phenomenon in different countries. They have also showed differentiating and strict reactions to this type of crime. The aim of the current study was to investigate the theoretical concepts and foundations of organized smuggling of goods in Iran and Turkey. It was also intended to study the legal approaches adopted by these countries to this type of crime along with the similarities and differences of their legal systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".