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Record W4410641666 · doi:10.1080/02732173.2025.2508796

Lineage norms, bride price-related factors, and intimate partner violence among Ghanaian women

2025· article· en· W4410641666 on OpenAlexaff
Eric Y. Tenkorang

Bibliographic record

VenueSociological Spectrum · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsMemorial University of Newfoundland
FundersHarry Frank Guggenheim Foundation
KeywordsLineage (genetic)Domestic violencePsychologySocial psychologyDating violenceIntimate partnerHuman factors and ergonomicsMedicinePoison controlBiologyGeneticsMedical emergencyGene

Abstract

fetched live from OpenAlex

There is growing research on bride price payment and intimate partner violence (IPV) among women in sub-Saharan Africa, but evidence is mixed, with some studies finding significant links between bride price and IPV, and others not finding links. A major limitation of these studies is the lack of attention to how lineage affiliations (matrilineal, patrilineal, bilateral) can influence bride price-related factors and IPV. This paper examines links between bride price-related factors and IPV across lineage groups among women in Ghana. The study used data from 1,111 currently married Ghanaian women from three main ecological areas. Binary logit models were used to examine the effects of bride price-related factors on IPV for matrilineal, patrilineal, and bilateral women. Results show Patrilineal women were more likely to experience physical, sexual, and emotional violence when bride price was paid in full than partially. Matrilineal and bilateral women only experienced emotional and physical abuse when bride price was paid in full. Patrilineal women who believed their male partners owned them because of bride price were less likely to experience all IPV types. Although other factors are important to understand the high prevalence of IPV in patrilineal women, bride price-related factors provide a useful explanatory pathway.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.293
Teacher spread0.274 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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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