The Comparison of Deterrence Punishment for Trade Violations in Ja’fari Jurisprudence and Iran Trade Laws
Bibliographic record
Abstract
Background and objective: In the present study, the comparison of deterrence punishment for trade violations in Ja’fari jurisprudence and Iran trade was examined. Also, the effectiveness of these punishments on reduction of trade violations and the necessity of institutionalizing religious orders in different trades were discussed.Methodology: the method used in the present study is descriptive-analytical and it used different trade laws and Islamic narrations and Hadith. According to the variety of trade violations in governmental and non- governmental sectors, seven important violations of hoarding, health violations, business fraud, not to include price, smuggling, use of short weights, and overcharge were discussed in the present study.According to the main objective of the executives of punishments for trade violators to support and observe consumer rights and also the study of Quranic verses, narrations and Hadith, showed that paying less attention to religious orders has a positive effect on trade violations. Inflation in commodity price and sanction of some consumable commodities has also a positive effect on reduction of trade violations deterrence. The results also showed that the effectiveness of trade violations deterrence is more than its legal resources according to religious and Islamic factors mentioned in Ja’fari jurisprudence. Thus, the necessity of approving a comprehensive trade system law with the approach of institutionalizing the culture of Islam based on trade laws of Ja’fari jurisprudence is felt.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".