The Career Length and Service of Female Policymakers in the US House of Representatives
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
Various studies have outlined the institutional (e.g. the existence of quota laws and the electoral system type of a country) and non-institutional factors (e.g. the political culture of a country) that account for variation in women’s representation, in general, and, in more detail, the low representation of women in the US Congress. However, no study has, so far, compared the Congressional career paths of men and women in order to understand whether this gender gap in representation stems from a difference in terms of the duration and importance of the careers of male and female policymakers. Using data on all US House elections between 1972 and 2012, we provide such an analysis, evaluating whether or not the political careers of women in the US House of Representatives are different from the political careers of their male counterparts. Our findings indicate that the congressional careers of men and women are alike and, if anything, women may even have a small edge over their male colleagues.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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