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Record W3193354906 · doi:10.6000/1929-4409.2021.10.155

The Troubling Epidemic of Wife-Battering in Ogbaru and Onitsha North Local Government Areas of Anambra State, Nigeria

2021· article· en· W3193354906 on OpenAlexvenueno aff
Chinwe Edith Areh, Benjamin Okorie Ajah, Oguejiofo C. P. Ezeanya, Ann Ugomma EZE, Stanley Ikenna Onwuchekwe, Chukwuemeka Dominic Onyejegbu

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsWifeDenialNonprobability samplingGovernment (linguistics)State (computer science)Local governmentSocioeconomicsLocal government areaSociologyDowryPsychologyPolitical scienceDemographyPopulationLawMathematics

Abstract

fetched live from OpenAlex

Purpose: Debates and assumptions on the trend, motives/causes and implications of wife-battering in Nigeria are largely speculative. The purpose of this article is to explore in a raw form, the socio-economic determinants of wife-battering, on the sub-areas of family violence. Methods: Using qualitative and quantitative research methods, a sample of 364 respondents comprising 196 males and 168 females was drawn from Anambra State, Nigeria. Multi-stage and purposive sampling techniques were used to reach the respondents. Questionnaire and in-depth interviews were instruments for data collection. Results: Findings confirmed that wife-battering in Ogbaru and Onitsha-North Local Government Areas is most often caused by denial of sex and infidelity. Conclusion: The policy implications calls for the creation of local government welfare units to be holding periodic talk shows for married couples on the imperative of living happily.

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 score0.061
Threshold uncertainty score0.437

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 studies0.0000.001
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 head0.057
GPT teacher head0.330
Teacher spread0.273 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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