Uncovering the Origins of the Gender Gap in Political Ambition
Bibliographic record
Abstract
Based on survey responses from a national random sample of nearly 4,000 high school and college students, we uncover a dramatic gender gap in political ambition. This finding serves as striking evidence that the gap is present well before women and men enter the professions from which most candidates emerge. We then use political socialization—which we gauge through a myriad of socializing agents and early life experiences—as a lens through which to explain the individual-level differences we uncover. Our analysis reveals that parental encouragement, politicized educational and peer experiences, participation in competitive activities, and a sense of self-confidence propel young people's interest in running for office. But on each of these dimensions, women, particularly once they are in college, are at a disadvantage. By identifying when and why gender differences in interest in running for office materialize, we begin to uncover the origins of the gender gap in political ambition. Taken together, our results suggest that concerns about substantive and symbolic representation will likely persist.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".