The Effect of Death on Dissolution of Marriage Contract with Emphasis on Presumed Death
Bibliographic record
Abstract
The dissolution of the marriage contract is either intentional or compulsory. The intentional dissolution basically takes place with divorce or termination application. But, the unintentional or compulsory dissolution means a marriage contract is dissolved automatically and without the will of the parties. The most important causes of unintentional dissolution include termination, death, expiration (in temporary marriages) and… which marriage contract can be dissolved by the occurrence of these and some other special causes. One of unintentional marriage dissolution causes is death. Death is divided into three groups of natural death, presumed death and constructive death. Iran's civil law has not pointed directly to constructive death, but beside other categories states its conditions and ordinance. There is no doubt that natural death triggers a marriage contract to be dissolved. There is disagreement among experts of Islamic rules and jurists on this matter if presumed death can dissolve a marriage or not. But, with study of legal rules related to missing person and the effects of the judgment rendered for presumed death, it seems that presumed death can dissolve the marriage contract too. And, the divorce application sets out at article 1029 of Iran's Civil Law relates to an occasion which inheritors have not applied from the court to issue a presumed death judgment.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".