Damage compensation for innocent defendants and Convicts in Iran and the Canadian legal system
Bibliographic record
Abstract
In the field of innocent defendants and convicts' damage compensation who have endured further losses due to issue criminal supply contracts or orders execution, their innocence has been cleared by issuing acquittance sentences. It counted as one of the most challenging issues in private and criminal law. In these recent years, based on positive changes in the rules of Iran, a lot of works done for innocent defendants and convicts' damage compensation have endured different and unfair punishments. But no integration or constructive work has been done for guiltless convicts' damage compensation who have endured some parts or all their punishments, and their innocence has been proved but not predicted. The reverse of this matter is true in the Canadian law system. Only a guilty convict who has tolerated some or all parts of unfair punishment deserves to receive damage compensation. This study attempted to research the subject's international binding rules, and many practical strategies for guiltless convicts' damage compensation will be considered in both systems by a comparative study.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".