Domestic Violence and Effective Strategies for Tackling its Consequences on Students’ Educational Aspirations in Kwara State, Nigeria
Bibliographic record
Abstract
This is a descriptive study that examined the prevalence of domestic violence and effective strategies for tackling its consequences on students’ educational aspirations in Kwara State, Nigeria. The sample for this study was made up of 200 students and 150 teachers drawn from ten secondary schools which spread across Kwara Central senatorial district, totaling 350 respondents for the study. Data for this study was collected through a questionnaire, two research questions were raised and answered and three research hypotheses were postulated and tested. Findings revealed that the prevalence of domestic violence among couples includes pouring acid on one another, the cutting of the manhood by the wife, stabbing one another, pouring hot water on one another, poisoning one another through food or other means, beating, kicking, and slapping the wife. Findings further revealed that effective strategies for tackling domestic violence in Kwara Central are tolerance, patience, an anti-domestic violence sensitization forum, avoiding third party involvement, and the creation of a guidance and counseling unit. Other strategies include the situation where the husband should be held responsible, extra marital affairs should be avoided, husbands should avoid acquiring many wives with a low income, family interference should be avoided, and couples should learn how to say sorry to each other. These would all help to tackle domestic violence in society. It was recommended that the Government should set up an enlightenment campaign for couples to help eradicate domestic violence. There should also be a punishment for those that violate the rules and regulations of tackling the menace of domestic violence. Religious leaders should intensify efforts to fight against domestic violence in the society at large.
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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 | 0.001 |
| 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".