Kinship and Intimate Partner Violence Against Married Women in Ghana: A Qualitative Exploration
Bibliographic record
Abstract
In African societies, kinship ties determine how women are socialized, their access to power and wealth, as well as custody of children, often considered important factors in married women's experience of intimate partner violence (IPV). Yet studies that examine how kinship norms influence IPV are scant. Using in-depth interviews collected from women identifying with both matrilineal and patrilineal descent systems, we explored differences in Ghanaian women's experiences of IPV in both kin groups. Results show that while IPV occurs across matrilineal and patrilineal societies, all women in patrilineal societies narrated continuous pattern of emotional, physical, and sexual assault, and their retaliation to any type of violence almost always culminated in more experience of violent attacks and abandonment. In matrilineal societies, however, more than half of the women recounted frequent experiences of emotional violence, and physical violence occurred as isolated events resulting from common couple disagreements. Sexual violence against matrilineal women occurred as consented but unwanted sexual acts, but patrilineal women narrated experiencing violent emotional and physical attack with aggressive unconsented sexual intercourse. Contextualizing these findings within existing literature on IPV against women suggests that policies aimed at addressing widespread IPV in Ghanaian communities should appreciate the dynamics of kinship norms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".