Who gets the prestigious positions? Credentials and ministerial appointments in Africa
Bibliographic record
Abstract
This study explores patterns of portfolio allocation and examines whether gender, education, professional background, and political experience predict the level of prestige associated with ministerial appointments that men and women receive. Using an original dataset of 4731 cabinet ministers across 25 African countries from 1990 to 2021, I find that, contrary to expectations, among the women appointed to cabinet in Africa, a higher proportion are assigned to high-prestige portfolios compared to men. Further analysis reveals that a person’s education and professional background do not directly correlate with being appointed to prestigious ministerial positions. Instead, gender and political experience are statistically significant predictors of the likelihood of being assigned to coveted portfolios. This study contributes to the gender and politics literature in three ways. First, it extends beyond merely quantifying the proportion of cabinet seats held by women to examine the sorts of ministerial positions they are appointed to compared to men. Second, it highlights how personal attributes affect ministerial appointments in Africa. Third, the study reveals that the gendered patterns of cabinet appointments seen in Europe and North America do not hold in Africa, highlighting the need to study underrepresented regions for a fuller understanding of gender and political appointments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".