Mapping the International Supply and Demand Side of Women’s Political Leadership in Crises
Bibliographic record
Abstract
Abstract In this article, we offer a theoretical framework outlining the conditions under which different types of crises, from wars to natural disasters, increase the probability of a woman ascending to a country’s highest office. We first draw on existing research to identify and develop four different supply and demand pathways that can confer a relative advantage to women running for executive office in times of instability: Traitcasting, Structural Shifts, the “Glass Cliff,” and Policy Recalibration. We then draw from the International Relations and Comparative Politics literatures to identify when each of these mechanisms is likely to be most salient. We suggest that a state’s domestic political institutions and norms along with the crisis characteristics operate as intervening variables, conditioning the salience of the four pathways. After outlining these intervening variables, the third section presents initial congruence tests on the rise of six women to executive office. In doing so, we provide preliminary evidence for this novel theoretical framework on women’s crisis leadership that can be further refined and empirically tested in future research.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".