A Trump Effect? Women and the 2018 Midterm Elections
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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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".