Criminal Liability and Crime and Punishment Proportionality in the Crime of Legal Entities
Bibliographic record
Abstract
One of the innovations of Islamic Penal Code in 2013 was to accept criminal liability of legal entities. By accepting criminal liability of legal entities, the way to punish them is arisen. As a legal person cannot commit any crime, any punishments are not applicable to them. Accordingly, Article 20 of this Law enumerated a list of penalties applicable to legal persons and it was tried to use penalties in accordance with the legal entities to deal with them. Punishments such as dissolution, confiscation, cash fine, announcement of the judgment, Diyeh, social and economic exclusion; such as a ban on business activities, prohibition of the public invitation to raise capital and ban from drawing business documents listed in Article 20 and Article 14, are a set of punishments which relatively different from usual punishment for individuals. These penalties are relative diversity, but what is objectionable is that the details and conditions of implementation of each of these punishments are not clear. If legislator described the details exactly or provided the condition to require the adoption of The Executive Bylaw of the punishment, it would be better. Given that all the points and issues about penalties for legal persons are not stated in this law as well as ambiguities in the law for a comprehensive definition of legal person, the way to implement main and supplementary punishments, In this study it was tried to evaluate and criticize the legal entities penalties including main and supplementary ones and their grading.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".