Safer If Connected? Mobile Technology and Intimate Partner Violence
Bibliographic record
Abstract
Mobile phones are an invaluable economic asset for low-income individuals and an important tool for strengthening social ties. They may also help women overcome physical boundaries, especially those who are separated from support networks and are bound within their husbands' social spheres. Using micro-level data on women and men from recent Demographic and Health Surveys, including new information on mobile phone ownership, this study examines whether women's ownership of mobile phones is associated with their likelihood of having experienced intimate partner violence (IPV) across 10 low- and middle-income countries. Findings show that women's ownership of mobile phones is associated with a 9%-12% decreased likelihood of emotional, physical, and sexual violence over the previous 12 months, even after controlling for characteristics proxying for socioeconomic status, household resources, and local development within the community. Estimates are negative in seven out of the 10 countries and results are robust to the use of nonparametric matching techniques and instrumental variables built through georeferenced ancillary sources. In exploring two potential mechanisms, I show that mobile phone ownership is positively associated with women's decision-making power within the household (decision-making power) and male partners' lower acceptability of IPV (attitudes). Findings speak to scholars and policymakers interested in how technology diffusion relates to dynamics of women's empowerment and global development.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".