Ready for a Woman President?
Bibliographic record
Abstract
Abstract Even though a record number of women ran for the Democratic nomination in 2020, Clinton’s loss in 2016 led pundits, party elites, and voters to worry about whether the country would be willing to support a woman for president, and polling organizations regularly asked questions that tapped into such concerns. While the vast majority expressed willingness to vote for a woman for president in polls, people were more skeptical about how their neighbors felt. Our research question cuts to the heart of this issue: How does polling information about comfort with the idea of a woman president affect perceptions of the electability of actual women running for their party’s nomination, and in turn voting decisions? We expect that exposure to signals of low comfort with a woman president will reduce perceptions of electability, and in turn dampen support for women at the nomination stage, but there are competing hypotheses for how signals of high comfort will be received. We further expect that Democratic women will be most affected by such information. We test these expectations with an experiment fielded on the 2019 Cooperative Congressional Election Study (CCES). Our findings have important implications for media coverage of polls related to women running for executive office.
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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 | 0.002 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".