A Look at Legislative Criminal Policy of Iran against Violent Crimes
Bibliographic record
Abstract
According to the opinions of the experts in the field of law, recognition of crimes and fighting against them is a social need combined with favorable political consequences that result in the acceptance and legitimacy of all political systems. The present article, with the descriptive-analytic method and application of all library resources, attempts to examine the designated policy of legal system of Iran in response to violent crimes. The results of the study indicate that the legislative-criminal policy of Iran includes different stages of prosecution, investigation and trial and even criminalization and sentencing. Populist criminal policy has been always considered in the Iranian legal system that instances of it can be observed in the act of intensification of punishments for the perpetrators of embezzlement, bribery and fraud. But in the late eighties and early nineties populist criminal policy emerged in the form of plans, bills and even multiple rules and unfortunately this trend is growing. At the end, it should be noted that in the populist legislative policies, due to lack of causal connection between the actions, the expected results are often not achieved and ineffectiveness of this policy on crimes and incapacity and inefficiency of this criminal policy is clear and obvious. It is hoped that the findings of this study can pave the way for further efforts in understanding and adopting wise policies and strategies to deal with all kinds of crime.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".