The Effective Mistake in Iran Fiqh and Jurisprudence
Bibliographic record
Abstract
The effect of mistakes on contract depends on certain conditions of which the most salient one is the fundamentality of mistakes. Magistrates must refer to contract parties' intentionsto identify the domain of the fundamental mistakes and their effects on the contract. Is the domain of the effective mistakes limited to the subject and important characteristics of the contract parties? Or can we find a unity of measurements among the characteristics of mistakes causing nullification, which incorporate all or at least most of the proofs of mistakes causing nullification? Why do in some cases, mistakes result in nullification and in others the cancel right and in some other ones no effects in contracts? The civil law, in articles 200 and 201, limits the domain of mistakes to "the contract itself" and "the important feature of contract parties". If we consider the base of the mistake effectsitsfundamentality, the mistake domain includes any mistakes in all basic elements of contracts which are the main reasons for making the contracts and mutual consent. Its condition is that the description of fundamentality is clearly or implicitly mentionedin the contract. Also the extent of effect of mistakes in contracts, depends on the importance of mistaken element in the opinions of contract parties. Some of the elements of the contract are related to mutual consent and mistakes in them result in problems in intention and nullifying the contract. Some other elements are not related to mutual consent and they are not in the domain of intention of the contract. If mistakes in them comes to the domain of mutual consent, naturally it leads to the authority of cancelling the contract.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.058 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| 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".