Why Won't Lola Run? An Experiment Examining Stereotype Threat and Political Ambition
Bibliographic record
Abstract
Among the most well-documented and long-standing gender gaps in political behavior are those relating to political ambition, as men have consistently been shown to express a significantly higher level of political ambition than women. Although this gap is well established, the reasons for the differences between men and women remain largely unknown. One possible explanation is that negative stereotypes about women's political ability are responsible. Stereotype threat, as it is referred to in the psychology literature, is a phenomenon where individuals of a social group suffer cognitive burdens and anxiety after being exposed to negative stereotypes that relate to their identity. These disruptions have been shown to alter attitudes and behavior. In order to test this possibility, we employed an experimental design whereby we randomly assigned 501 undergraduate students into threat and nonthreat conditions. While men exhibited higher levels of political ambition in both conditions, women in the nonthreat condition expressed significantly higher levels of political ambition than those women who were exposed to negative stereotypes. The results of this study therefore suggest that the gender gap in political ambition may be partly explained by negative stereotypes about women in politics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".