Legal Basis of Common Approaches to Object to a Criminal Judgment in Iran and England Penal Systems
Bibliographic record
Abstract
One of the basic discussions in criminal procedure code which has a direct relation with defendants’ rights in civil procedure process is the matter of objection to criminal judgments that have seriously changed and transformed after the Islamic Revolution. According to the criticisms received by Iran's legal procedure system, the legislator has tried to make closer their position to the world’s standards in the field of objection to criminal judgments by referring to its former rules especially the law of criminal trials’ principles in the law of criminal procedure code approved in 2013. In addition to the final nature of the sentences in common law system, today, different ways of objection are predicted in England accusatory system. The present research tries to deal with the matter that on the prediction of common ways of objection how much its legal basis is considered and how much Iran and England legislators succeed in this path, in addition to analyzing the real examples of the ordinary ways of projection (objection, research appeal, and review appeal) and legal foundations of each one of them in two penal systems of Iran and England. The results of the cases above can be the guide of Iran's legislator in approving and reforming the regulations related to the objection the votes and approximating the regulations to world’s criteria in this field.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".