Intimate Partner Violence (IPV) in Zambia: An Examination of Risk Factors and Gender Perceptions
Bibliographic record
Abstract
Abuse of women has reached epidemic proportions in Africa and the literature has revealed that, internationally, between 10% and 52% of women experience this phenomenon. This study examines some social risk factors associated with wife abuse, as well as gender perceptions of wife beating in Zambia, East Africa. Using the 2001/02 Zambia Demographic and Health Survey data, married women, age 15-49, were selected for analysis (N = 4731). The dependent variable in the study is wife abuse and explanatory variables are age, education, employment status, place of residence, beliefs about wife beating, and religion. The data show that more wives than husbands are likely to justify wife-beating under various circumstances with “going out without telling him” being the predominant belief for justifying beating. The data also revealed that in a multivariate context, wives with younger husbands, duration of marriage, place of residence, cultural beliefs about wife-beating, and membership in non-Orthodox religions are some of the risk factors that relate to intimate partner violence in Zambia. Implications of the study relate to strategies for dealing with this social problem, some of which are suggested in the paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".