Gender Affinity Effects in Vote Choice in Westminster Systems: Assessing “Flexible” Voters in Canada
Bibliographic record
Abstract
Under certain conditions, women are more likely than men to vote for women candidates, a phenomenon referred to as a “gender affinity effect.” Causal mechanisms connecting women voters to women candidates are gender consciousness, desire for descriptive representation, support for liberal social policy, the use of gender as a shortcut to vote choice among low-information voters, and a “party-sex overlap.” Existing work is focused on American elections, which tend to be candidate centered, so little is known about gender affinity effects between voters and candidates in other contexts. This article focuses on Westminster-style parliamentary systems, using the Canadian federal elections of 2000 and 2004 as test cases. Women in these systems have the same motivations to gravitate toward women candidates, for they are gender conscious and desire descriptive representation. But they do not have the same incentives to cast ballots for women because political institutions and practices tend to discourage candidate-based voting. The article pays particular attention to a segment of the electorate we call “flexible” voters, which is comprised of independents, leaners, and defectors. In Westminster systems, it is this group of voters who should be most sensitive to candidate-based considerations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".