Gender, Political Knowledge, and Descriptive Representation: The Impact of Long‐Term Socialization
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 Successive studies have found a persistent gender gap in political knowledge. Despite much international research, this gap has remained largely impervious to explanation. A promising line of recent inquiry has been the low levels of women's elected representation in many democracies. We test the hypothesis that higher levels of women's elected representation will increase women's political knowledge. Using two large, comparative data sets, we find that the proportion of women elected representatives at the time of the survey has no significant effect on the gender gap. By contrast, there is a strong and significant long‐term impact for descriptive representation when respondents were aged 18 to 21. The results are in line with political socialization, which posits that the impact of political context is greatest during adolescence and early adulthood. These findings have important implications not only for explaining the gender knowledge gap, but also for the impact of descriptive representation on political engagement generally.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| 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