The Strict Criminal Liability in Iran’s Criminal Legal System
Bibliographic record
Abstract
The strict criminal liability is one of the institutions which has been accepted and expanded its dimensions in countries, including England, since 19th century. Although, criminal intent or fault is the most important elements of every offenses, but, due to some reasons, such as expediency, necessity, benefit, increasing prevention index, the mens rea element of offense, fully or partially, is sometimes removed or presumed. In Iran’s criminal legal system, this legal institution has not been considered formally and explicitly by the legislator, but recently, Iranian legal experts, given some existing necessities, try to put some offenses into the category of the strict criminal liability. In fact, some offencesin criminal legal system, namely traffic offences, bounced cheque, injuries resulted from medical operations and some environmental offences, can be placed under the title of the strict criminal liability. Despite many problems to which the acceptance and development of the strict criminal liability may be faced, It seems, in many cases, this institution can solve the problems related to difficulties of proof and ascertainment of the criminal intent beforecourts. Also, today, Iranian judicial courts don’t take into account the mens rea practically and presume itin many cases.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".