Equal Participation but Separate Paths?: Women's Social Capital and Turnout
Bibliographic record
Abstract
Conventional models of voter turnout lead us to expect men to vote in greater numbers than women. Yet in advanced industrialized democracies, women tend to participate in elections as much, or more, than men do. This study addresses this puzzle by drawing on the social capital literature to help explain the paradox of voter turnout for women. Women are in fact “rich” in various forms of social capital, especially more informal networks of reciprocity which are often viewed as apolitical and not measured in resource models of voter turnout. Drawing on the Canadian National Survey of Giving, Volunteering, and Participating (NSGVP), the findings show that informal social capital helps explain why women turn out to vote at similar levels as men, despite having fewer traditional resources at their disposal. Hence, this study provides evidence that women's path to participation is different than men's. The author would like to thank Jillian Evans who provided helpful feedback on earlier drafts of this article.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".