Bibliographic record
Abstract
Introduction Whereas many academic discourses and popular debates pitt Hindu and Muslim laws as opposite and dissimilar to one another, this chapter, based on the analyses of trends in matrimonial disputes in Hindu and Muslim personal laws, demonstrates that there are many commonalities between judicial bargaining, interpretation, and treatment of cases filed under distinct provisions of Hindu or Muslim religious laws. Indeed, the Hindu and Muslim conjugal families are formed along similar, though not identical lines. In addition, whereas feminist and legal scholars have long assumed that Muslim Personal Law is detrimental to women's interests in part because of the Shah Bano case and the introduction of the Muslim Women's (Protection of Rights on Divorce) Act 1986, we find here that the data do not demonstrate a wide variation between rights accorded to Hindu and Muslim women; in fact, divorced Muslim women have more rights than divorced Hindu women in some instances. This chapter examines the adjudication process in Hindu and Muslim personal laws in state law and courts, analyzes the substantive and procedural aspects of matrimonial provisions in Hindu and Muslim personal laws, and gives an overview of the relevant trends in matrimonial disputes. It discusses the function of the Family Court in Mumbai, traces the interaction between state and society within the formal legal system, and assesses its impact on the construction of the conjugal family and gender equality in state law.
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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".