Do Elites Discriminate against Female Political Aspirants? Evidence from a Field Experiment
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 Do elites exhibit gender bias when responding to political aspirants? Drawing on theories of gender bias, group attachment, and partisan identity, I conduct the first audit experiment outside the United States to examine the presence of gender bias in the earliest phases of the political recruitment process. Based on responses from 1,774 Canadian legislators, I find evidence of an overall gender bias in favor of female political aspirants. Specifically, legislators are more responsive to female political aspirants and more likely to provide them with helpful advice when they ask how to get involved in politics. This pro-women bias, which exists at all levels of government, is stronger among female legislators and those associated with left-leaning parties. These results suggest that political elites in Canada are open to increasing female political representation and thus should serve as welcome encouragement for women to pursue their political ambitions.
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 | 0.000 | 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) | 0.001 | 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