Bibliographic record
Abstract
In today’s democracies, disempowered group members are no longer formally barred from the political arena. However, there is a concern that the historical memory of political inequality and exclusion remains as internalized cognitive dispositions, shaping behavior even after laws are changed. Focusing on the legacy of women’s political exclusion from the public sphere, I consider whether internal exclusions undermine women’s ability to influence political discourse even under conditions of formal political equality. All else being equal, do women and men in Western democracies have the same discursive influence? Are women particularly sensitive to men’s discursive authority? I help answer these questions using an experimental research design. The results of my study offer evidence that people are more willing to revise their opinions after hearing a man’s counterargument than after hearing a woman’s identical counterargument. This pattern appears to be driven by the way women respond to a man’s counterclaim. I discuss how gendered discursive inequities reinforce existing patriarchal structures, and the role that women inadvertently play in their own subjugation. I conclude by offering suggestions for better approximating the ideal of discursive gender equality.
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.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".