Criminological Analysis of Tax Evasion in the Criminal Law in Iran
Bibliographic record
Abstract
Tax evasion as one of the examples and symbols of corruption disturbs economic security needed to expand economic activities and investment. Therefore, amending and revising the tax system of country especially tax penal system in order to improve the national economy is considered essential and necessary issue. The purpose of choosing this study is criminological study of tax evasion in the criminal law and causes of tax evasion violation. The methodology of this study is descriptive analysis and library. In the method of analyzing first general framework is list drawn necessary divisions is taken with regard to the limitation issue Then the necessary resources provided and also consideration of the reliability and validity of them and the study, taking notes and summarizing also begun, in the end, the entire contents collected, analyzed and composited, and meanwhile revising, the final editing has been done. According to the results of research on the criminal law in Iran on charges of tax evasion and tax issues in the Islamic system, special attentions have been paid. Also the results show that the changing profile of the taxpayer is considered one of the ways of tax evasion and with situational, social and legal preventions this crime could be reduced.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".