Determining the Advantages and Disadvantages of the Iranian Legal System's Selection Policy in Response to Violent Crimes
Bibliographic record
Abstract
In addition to being a social need, fighting against crime, has a political function and provides the acceptance and legitimacy of political systems. For this reason, the government’s policy of suppression, reduction and prevention of crime, concerns the public opinion. It is necessary that, the best and the most efficient way is selected to fight crime with the help of scientific technology. In the present article entitled “Determining the advantages and disadvantages of the legal system's selection policy in response to violent crime” we have been trying to serve the purpose of determining the advantages and disadvantages of the legal system's selection policy in response to violent crimes and explain the best way to fight against violent crimes, taking into account social needs, the analytical methods and the use of all library resources to answer the questions; what processes has the criminal policy of Iran regarded to prevent violent crimes? How has the Populist criminal policy been expressed in legislation of Iran? Finally, what are the results of the populist criminal policy? The results of the study show that the Populist criminal policy has always existed in the Iranian legal system, the example of which is seen in the law of intensification of punishments for the perpetrators of embezzlement, bribery and fraud.
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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 | 0.006 | 0.019 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".