Do Women Politicians Know More about Women’s Policy Preferences? Evidence from Canada
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 This study draws together theories of women’s substantive representation and research on politicians’ knowledge of constituent preferences. We ask whether politicians are better at predicting their constituents’ policy preferences when they share the same gender. In doing so, we contribute to knowledge about the mechanisms underlying substantive representation. Using original surveys of 3,750 Canadians and 867 elected politicians, we test whether politicians correctly perceive gender gaps in their constituents’ policy preferences and whether women politicians are better at correctly identifying the policy preferences of women constituents. Contrary to expectations from previous research, we do not find elected women to be better at predicting the preferences of women constituents. Instead, we find that all politicians — regardless of their gender — perform better when predicting women’s policy preferences and worse when predicting men’s preferences. The gender of the constituent matters more than the gender of the politician.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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