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Record W2288692974 · doi:10.1111/1467-8268.12169

The Threat of Domestic Violence and Women Empowerment: The Case of West Africa

2016· article· en· W2288692974 on OpenAlexaff
Saikou Amadou Diallo, Marcel Voia

Bibliographic record

VenueAfrican Development Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsDomestic violenceEmpowermentResidenceWifeEconomic growthPolitical scienceGeographyDevelopment economicsSocioeconomicsDemographic economicsPoison controlSuicide preventionEconomicsEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

This paper assesses the significance of a set of threats of domestic violence in ten West African countries that arguably limit the potential of women in particular and the development of society. The data consists of a country-specific Demographic and Health Survey (DHS), conducted in the same way for each participating country. The risk of domestic violence and the intensity of its threat are assessed using different probabilistic model specifications together with an assessment of how heterogeneous/homogenous are these effects across the set of countries. The overall results suggest that religion has played a significant role in relation to domestic violence in most countries. Area of residence (rural) has played an important positive role as well. The lack of education increases the threat, and level of well-being and/or household's level of wealth have a significant negative impact on the threat of domestic violence in general. Finally, the factor that defines the improvement in wife's social status which is characterized by the wife's higher level of education has also been important in reducing the threat of domestic violence.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0010.001
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 head0.018
GPT teacher head0.281
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2016
Admission routes1
Has abstractyes

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