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Record W7082450555 · doi:10.5281/zenodo.17170146

WHEN MARRIAGE BECOMES A MOTIVE: MARITAL HOMICIDES, CULTURAL SILENCE, AND LEGAL FAILURE IN INDIA

2025· article· en· W7082450555 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languageen
FieldComputer Science
TopicMathematics, Computing, and Information Processing
Canadian institutionsnot available
Fundersnot available
KeywordsWitnessRomanceHomicideShameArgument (complex analysis)Civilization

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.799
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.234
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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