Political violence and women’s political participation in Kenya
Bibliographic record
Abstract
For many emerging democracies, violence plays a major role in the political sphere as it becomes ingrained in the logic of what it means to be political. Political violence disrupts democratic processes and prevents marginalized groups, particularly women, from fully expressing their political agency. This study examines the relationship between political violence and women's political participation in Kenya. The study employs data from the eighth round of Afrobarometer, which surveyed 2,400 respondents. We analyzed the interaction effects between gender and experiences of political violence on protesting and voting behaviours. The findings reveal that women are significantly less likely to protest (11% decline) or vote (12% decline) following experiences of violence, whereas men exhibit opposite trends, with an 11% increase in protest participation and a 12% increase in voting. These gendered divergences highlight how political violence disproportionately hinders women's political engagement while reinforcing male dominance in the political sphere. The study concludes by exploring strategies women can adopt to navigate hostile political environments, including leveraging online platforms for mobilization and fostering community-based safe spaces. This study ultimately finds that political violence is associated with political participation, with several gendered ramifications.
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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) | 0.005 | 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".