Domestic Violence against Women: Statistical Analysis of Crimes across India
Bibliographic record
Abstract
Due to the difficulties involved in obtaining data, especially on a nation-wide basis, statistical studies of domestic violence have been limited. This is particularly true for developing countries, including India, where gender discrimination is deeply entrenched. This study draws on data from one of the only current sources of nationwide information about domestic violence in India, the Indian National Crime Records Bureau. To examine the general patterns in domestic crimes against women across India, multivariate linear regression is performed on Dowry Death (wife murder) and Cruelty (wife abuse) crime rates for the major Indian states and cities. The primary findings reveal a robust inverse relationship between Dowry Death crimes and a state’s level of development, and suggest a possible link between development and other domestic violence. Specifically, the urban-rural disparities in the Cruelty data are explained with a “gendered resource theory” hypothesis that wife-abuse is more prevalent in areas of higher social development change (such as changing gender roles). The paper discusses these results within an ecological domestic violence paradigm, and addresses the inherent complications with using crime data.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".