PROTECTING A WIFE FINANCIALLY AT THE TIME OF DIVORCE – A COMPARISON BETWEEN SOUTH AFRICAN WOMEN MARRIED IN TERMS OF SOUTH AFRICAN CIVIL LAW AND ISLAMIC LAW, WITH SPECIFIC REFERENCE TO THE MAHR
Bibliographic record
Abstract
This article compares the South African civil-law and Islamic-law positions with regard to the financial protective measures available to a wife at the time of marriage and divorce. In this regard, the respective matrimonial property systems are discussed, with special emphasis on civil antenuptial and Muslim marriage contracts. In addition, other protective measures inherent to the two systems to prevent prejudice both during the marriage and at the time of divorce, are discussed. It is submitted that, although the provisions of Islamic law do not provide the same financial protection for wives compared to the South African civil law, the Islamic concept of mahr could potentially be used in the Muslim marriage contract to enhance financial security of a Muslim wife at the time of divorce. The article also considers dual marriages where the same spouses marry each other in terms of both civil and Islamic law. In particular, the incorporation of the Islamic concept of mahr into civil antenuptial contracts is discussed with reference to the legal position in Canada to illustrate potential legal problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".