A Qualitative Study To Investigate Male Victims’ Experiences Of FemalePerpetrated Domestic Abuse In India With Reference To Gwalior Chambal Division
Bibliographic record
Abstract
Despite evidence of a significant increase in violence against men by women, female penetrating domestic violence against men has not been studied in India. This study used a qualitative exploratory descriptive approach to investigate male victims of female-perpetrated domestic violence in India, especially Gwalior - Chambal Division, Madhya Pradesh. Semistructured interviews were utilized to gather data from 33 married males in the Gwalior Chambal division, which was subsequently analysed using an interpretive phenomenological approach. There were 5 themes identified: 1. the causes of domestic assault against men; 2. the different types of domestic abuse against men; 3. the consequences of domestic abuse against men; 4. men's ideas of reducing or stopping abuse by their husbands; and 5. the characteristics of abusive wives. Participants experienced varying degrees of mental, behavioural, and verbal harm, coercive control, emotional neglect, and physical assault, all of which had an impact on them and their families. Abusive women used a number of techniques to justify their behaviour, including sex, children, solitude, and money. Furthermore, individuals were persuaded to divorce, stay in an abusive relationship, or use violence against their wives by clan and traditional values, societyinstitutions, and norms. The top causes for male violence were wives' disregard of the house, children, appearance, and personal hygiene; squandering money; wives' family meddling in the couple's private marital relationships; the wife's betrayal; and traditional thinking. New perspectives on domestic violence must be developed in India to help us better understand the nature of abuse against men, provide resources and assistance to them, reduce the prevalence of domestic abuse, and protect Indian married males.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".