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Men's Self-Reported Experiences of Women's Controlling Behaviours and Intimate Partner Violence in Kenya

2023· book-chapter· en· W4385540969 on OpenAlexaff
Eric Y. Tenkorang, Alice Pearl Sedziafa, Sitawa R. Kimuna

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsWestern University
Fundersnot available
KeywordsKenyaDomestic violencePhysical abusePsychologyDemographyPsychological abuseSexual abuseMedicinePoison controlInjury preventionEnvironmental healthPolitical scienceSociology

Abstract

fetched live from OpenAlex

Abstract In spite of the growing evidence of intimate partner violence (IPV) against men, limited scholarly work exists on this topic. To date, few studies have explored the motivations and socio-cultural underpinnings of violence against men in Kenya and sub-Saharan Africa in general. Using the Kenya Demographic and Health Survey and employing logit models, we examined associations between women's controlling behaviours and IPV among 3,262 Kenyan men aged 15–54 years. Over 60% of the Kenyan men surveyed reported their female partners were controlling. Compared with those who did not, men who reported controlling behaviours were significantly more likely to have experienced three types of violence (physical, sexual and emotional). Educated Kenyan men had higher odds of experiencing physical and emotional violence than the uneducated, and they reported higher levels of control by their female partners. Our findings suggest that IPV against men may be goal-oriented, but there is also evidence that it may be a reaction to male-perpetrated abuse.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.027
GPT teacher head0.307
Teacher spread0.280 · 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 designQualitative
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

Citations3
Published2023
Admission routes1
Has abstractyes

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