Bibliographic record
Abstract
Abstract Although women and men enjoy formally equal political rights in today's democracies, there are ongoing gaps in the extent to which they make use of these rights, with women underrepresented in many political practices. The gender gap in democratic participation is problematic because gendered asymmetries in participation entail collective outcomes that are less attentive to women's needs, interests, and preferences. Existing studies consider gender gaps in voting behavior and in certain forms of nonelectoral politics such as boycotting, signings a petition, or joining a protest. However, almost no work considers gendered variation in discursive politics. Do women participate in small, face-to-face political discussion groups at the same rate as men? And does gender intersect with other identities—such as ethnicity—to impact attendance at political discussion groups? I use data from the Canadian Election Study 2015 Web Survey to answer these questions. I find that women are significantly less likely to attend small-group discussions than men and that ethnicity intersects with gender in some important ways. However, I find no evidence that other social attributes—poverty or the presence of young children in the home—suppress women's participation in political discussion groups more than men's.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".