WHEN MARRIAGE BECOMES A MOTIVE: MARITAL HOMICIDES, CULTURAL SILENCE, AND LEGAL FAILURE IN INDIA
Bibliographic record
Abstract
ABSTRACT Marital and romantic entanglements have become one of the leading motives of homicide in India, with NCRB data indicating that one out of every fifteen murders arise from intimate relationships¹ This article explores the disturbing rise of lethal intimacy, using case studies such as the 2024 Telangana homicide and the Indore contract killing to demonstrate how secrecy, cultural stigma, and institutional failures converge into violence. Drawing from sociological data, judicial observations, and comparative frameworks, the article identifies three toxic forces (1) premarital concealment, (2) divorce stigma, and (3) weaponized gender expectations that turn marriage certificates into potential death warrants. Legal institutions exacerbate the crisis through evidentiary lapses, witness intimidation, and procedural delays. Furthermore, rising right-wing cultural narratives have deepened shame around divorce and love marriages, pushing couples toward repression rather than resolution. By engaging comparative reforms from Britain, Canada, and Japan, the article proposes a Marital Safety Bill, stronger witness protection, and cultural reorientation from log kya kahenge?(“what will people say?”) to hum kya kar sakte hain? (“what can we do?”). The argument is urgent: no civilization can claim greatness when its homes become abattoirs and its wedding albums double as murder exhibits. Keywords – Marital homicide in India, Intimate partner killings, Divorce stigma and violence, Cultural shame, love marriages.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".