Kinship, lineage resources (wealth flow transfers), and intimate partner violence among women in Ghana
Bibliographic record
Abstract
Previous research has established relationships between lineage and intimate partner violence (IPV). The findings suggest matrilineal women experience less IPV than patrilineal women. However, the IPV outcomes of bilateral women are unknown because of the limited operationalization of lineage with ethnicity. In our study, we used self-reported and multidimensional measures of lineage to explore its relationship with IPV, focusing particularly on the mechanisms linking the two. We hypothesized that wielding resources would be negatively associated with IPV. Furthermore, matrilineal women's access to lineage resources would reduce their vulnerability to IPV relative to patrilineal women. To examine these hypotheses, we collected data from 1700 ever-married Ghanaian women residing in three ecological zones (coastal, middle, northern). Path analysis was used to explore resources as mechanisms linking lineage and IPV. Our findings indicated resources were patterned by lineage. Matrilineal women benefitted more from maternal family members than patrilineal women and vice versa. Consistent with the standard resource theory, women's access to resources protected against IPV, and the effects were stronger for matrilineal than patrilineal women. Irrespective of how lineage was measured, matrilineal women experienced lower levels of IPV than patrilineal women. The IPV outcomes for bilateral women were mixed. Part of matrilineal women's reduced IPV risk was explained through access to maternal resources. While patrilineal women experienced higher levels of IPV, this was reversed with resources from paternal kin members. Our findings suggest that as resources are fundamental to reducing IPV, lineage can serve as a conduit for resource exchange and wealth transfer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".