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Record W2308760378 · doi:10.1108/ijssp-03-2015-0033

Policy suggestions for combating domestic violence in West Africa

2016· article· en· W2308760378 on OpenAlexaff
Paul Alhassan Issahaku

Bibliographic record

VenueInternational Journal of Sociology and Social Policy · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsContext (archaeology)LegislaturePolitical scienceEconomic growthSociologyDevelopment economicsGeographyLawEconomics

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to assess West African countries’ approach to address the issue of domestic violence (DV) in order to identify limitations and suggest policy measures. The paper situates DV in West Africa in the context of international literature and examines the question: what are the limitations of approaches to combating DV in West Africa and what is the way forward? The paper focusses on Ghana as a case example of efforts at addressing DV in West Africa. This is because Ghana is a pioneer among the very few West African countries that have developed a legislative cum policy framework to combat DV. A critical review of Ghana’s approach provides useful lessons for the way forward on policy against DV in the West Africa subregion. Design/methodology/approach – The methodology adopted consists of a survey of existing literature – theoretical and empirical – on DV in the international and Ghanaian contexts, a critical reflection on Ghana’s DV law, and synthesis of the emerging knowledge combined with familiarity with the context to make policy suggestions. A general review of literature on DV provides background understanding of the phenomenon globally and in the context of West Africa. Then an examination of Ghana’s law against DV helps to identify the limitations of the legislative approach. Finally, the paper makes suggestions on how to combat DV in West Africa at large. Findings – There is a high prevalence of DV in West Africa, particularly violence against women, although men also experience it. Some countries in the subregion, Ghana being an example, have adopted a legislative approach to deal with the problem. This approach criminalizes DV and requires victims or witnesses to report to the police. Perpetrators may be arrested and arraigned before a court and, if found culpable, fined or imprisoned while victims are promised protection and subsistence. The legislative approach is reactionary and cold, requiring reporting of violence even though this is not culturally expedient. The approach also frustrates victims who are willing to report by being cumbersome and costly. Finally, the approach is not built on any notable theory of DV. Research limitations/implications – The findings reported in this paper are based on secondary information. As a result, the analysis and conclusions are limited to what could be drawn from the documents reviewed and the experience of the author. Practical implications – The paper suggests specific measures for combating DV in West Africa. These include setting up a national taskforce on DV to coordinate actions and activities toward ending violence, using traditional, and religious leadership structures to campaign against DV, designing mentoring groups for men and women who are preparing to get into marriage, using social workers instead of the police to support victims of violence, institutionalizing assessment and care for DV victims at the hospital, and setting up funding for DV research. These measures could go a long way in combating DV in West Africa. Originality/value – This critical assessment of the legislative approach to combating DV in West Africa is about the first of its kind and therefore makes an original contribution to the literature. Also, the specific measures suggested in the paper are rare in reviews of its kind and therefore offers something of great value to policy makers and professionals in West Africa.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.404
Teacher spread0.370 · 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 designTheoretical or conceptual
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

Citations5
Published2016
Admission routes1
Has abstractyes

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