All the President’s Women? Female Leaders, Family Ties, and Gendered Cabinet Appointments Worldwide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study investigates the nexus between the rise of female leaders and the appointment of women to cabinets and how family ties, crucial for women’s political ascendance, impact these appointments. Using a unique dataset across 160 countries from 1966 to 2021, we find that female leaders generally appoint more women to their cabinets and key cabinet roles. However, this effect is significantly moderated by the “Goldilocks” principle, defined by the nature of a leader’s family ties. Specifically, female leaders with moderate family ties are most likely to appoint women. In contrast, their counterparts from political dynasties and those without familial political ties are less inclined to do so. The exploratory analysis suggests potential mechanisms driving this dynamic: female leaders with a “just-right” degree of political lineage are more likely to have advanced degrees and Western education, potentially aligning them more closely with liberal and feminist values.
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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 | 0.001 | 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 | 0.001 |
| 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 it