Analyzing the relationship between the experience of intimate partner violence and female internet use in Nigeria
Bibliographic record
Abstract
Female internet use is abysmally low (17.48%) in Nigeria, and it has become an issue of serious concern due to its negative impact on their health and economic well-being. While most of the scholarly debates centers on the influence of individual socio-economic characteristics and geographical factors to understand and improve female internet use, this study contributes to the literature by interrogating the role of women's experience of intimate partner violence (IPV) and other socio-economic and geographical factors based on the social theory of internet use. We conduct spatial and logistic regression analyses using the 2018 cross-sectional Demographic and Health Survey of 41,821 women aged 15–49. The spatial analysis shows a significant concentration of female internet non-use in the Northern region relative to the South, while the binary logistic regression analysis indicates that the experience of IPV, age, wealth status, education, being married, urban location, and residing in Northern Nigeria are significant predictors of female internet use. The study recommends tailoring support systems and interventions that acknowledge the unique challenges faced by survivors of IPV and the improvement of the socio-economic conditions of women to achieve greater internet use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".