Mobile Phones and Attitudes Towards Women’s Participation in Politics: Evidence from Africa
Bibliographic record
Abstract
This study explores the relationship between technology adoption and attitudes towards gender equality in political representation by relying on diffusion theories coupled with frameworks of ideational change, social interaction, and world society. We examine whether the use of mobile phones shapes gender attitudes towards women’s participation in politics by making it more widely accepted that women hold institutional roles. We do so by using micro-level data from the AfroBarometer, covering 36 African countries and adopting a multilevel modeling approach. Our results suggest that regular use of mobile phones is associated with more positive attitudes towards women’s participation in politics. The significant relationship – robust to the use of instrumental variable (IV) techniques – is observed only among women. This finding strengthens the idea that technology adoption on the part of women, by improving connectivity and expanding access to information, may be a successful lever to raise women’s status and promote societal well-being, ultimately contributing to achieving Sustainable Development Goal (SDG) 5 that seeks to “achieve gender equality and empower all women and girls.” Concurrently, the lack of a significant relationship for men highlights an important yet often neglected issue: policies aimed at changing gender attitudes are often targeted towards women, but men’s attitudes can be stickier than women’s, thus requiring further attention.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".