Bibliographic record
Abstract
The participation of women in governance plays a crucial role in fostering democratic, egalitarian, and sustainable societies (Agbalajobi, 2010; Simbine & Oyekanmi, 2025). Despite this, data consistently demonstrates that women’s representation in leadership positions across Africa remains below parity (Brookings, 2023; UN Women, 2025). While some countries, such as Rwanda, Kenya, and South Africa, have made notable strides in increasing female political representation, gender disparities persist across various economic and political sectors (IMF, 2023). This disparity exists notwithstanding the implementation of global, regional, and national policies aimed at promoting women’s rights and ensuring their equal participation in political and economic spheres). According to the 2021 report, Africa’s female political representation stands at just 24%, highlighting the ongoing need for targeted interventions (International IDEA, 2021). Factors such as entrenched patriarchy, lack of political will, and restrictive electoral frameworks continue to hinder progress towards gender parity in governance. Addressing these barriers requires concerted efforts from governments and development organisations to empower women and enhance their access to political leadership (Sadie, 2015).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.315 | 0.175 |
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".