Penal Mediation to Dissolve Discord among Peasants in Guilan (Iran)
Bibliographic record
Abstract
Present research was accomplished to survey penal mediation role in dissolving discord among peasants in Guilan province of Iran. Restorative justice is to make all parties participate in discord dissolution process and to decriminalize it with tools like mediation. It is based on a principle in which no culprit is pursued and also it is planning to relief victim. The law of criminal procedure in Article 82, projects “mediation” subject in crime deterrent grades 6, 7, 8. These crimes usually are pardonable or at least private complainer pardon is effective in mitigation. This issue causes reduction of criminal files and also criminal costs. It facilitates the social revive of the criminal. Modern criminal justice believes that penal mediation as one of settlement methods should follow special regulations which guarantee criminal and victim rights. This research is presented in four sections. This research is practical and the method is descriptive - analytical. Statistical population is consisting of 160 persons from many different villages in Guilan province. In order to collect data, questionnaire was administered and data analysis was performed using SPSS software. In forth section of this research, considering related questions, we were after to prove hypotheses. Results showed that criminal mediation can be settled by meetings performed by elders of villages in Guilan province and it prevents fights and claim .As a new look of criminal justice, it can be used as an appropriate instrument for judiciary.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".