A Trump Effect? Women and the 2018 Midterm Elections
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 From the moment Donald Trump took the oath of office, women’s political engagement skyrocketed. This groundswell of activism almost immediately led to widespread reporting that Trump’s victory was inspiring a large new crop of female candidates across the country. We rely on a May 2017 national survey of “potential candidates” and the 2018 midterm election results to assess whether this “Trump Effect” materialized. Our analysis uncovers some evidence for it. Democrats – especially women – held very negative feelings toward Trump, and those feelings generated heightened political interest and activity during the 2018 election cycle. That activism, however, was not accompanied by a broad scale surge in women’s interest in running for office. In fact, the overall gender gap in political ambition today is quite similar to the gap we’ve uncovered throughout the last 20 years. Notably, though, about one quarter of the Democratic women who expressed interest in running for office first started thinking about it only after Trump was elected. That relatively small group of newly interested candidates was sufficient to result in a record number of Democratic women seeking and winning election to Congress. With no commensurate increase in Republican women’s political engagement or candidate emergence, however, prospects for gender parity in US political institutions remain bleak.
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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