Bibliographic record
Abstract
Over the years, there has been a decline in the rate of political participation among African women, reflected in the widening gender gaps in civic and political participation. Various scholars have noted the integral role political institutions and context play in women’s political participation, but only a handful of studies have examined how the political context informs different forms of conventional and unconventional political participation. There is a growing body of research on the influence of the political context and institutions on African women’s political agency. In this study, the political context captures the extent to which a political system is open to claim makers and the capacity for state repression. My dissertation examines how political opportunity structures influence political participation among African women. The study employs a mix of quantitative and qualitative methods to address its objective. The quantitative dimension is based on a multilevel analysis of the three rounds of Afrobarometer datasets from 2011 to 2018. My qualitative analysis focuses on interviews with 50 participants during the October 2020 #EndSARS protest in Lagos State, Nigeria. Preliminary findings found nuanced results on the influence of political context on the gender gaps in political participation. Analysis from the interviews revealed that despite the hostile political climate in Nigeria, protesters defied the state to express their political angst and agency during the #EndSARS protests.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".