Digging Deeper into the Gender Gap: Gender Salience as a Moderating Factor in Political Attitudes
Bibliographic record
Abstract
Abstract We know how sex (rather than gender) structures political preferences, but researchers rarely take into account the salience or importance of gender identity at the individual level. The only similar variable for which salience is commonly taken seriously is partisanship, for which direction and importance or strength are both considered imperative for measurement and analysis. While some scholars have begun to look at factors that may influence intragroup differences, such as feminism (Conover, 1988), most existing research implicitly assumes gender salience is homogenous in the population. We argue that both the content of gender identity (that is, what specifically is gender identity, as opposed to sex) as well its salience should be incorporated into analyses of how gender structures political behaviour. For some, gender simply does not motivate behaviour, and the fact that salience moderates the impact of gender on behaviour requires researchers to model accordingly. Using original data from six provincial election studies, we examine a measure of gender identity salience and find that it clarifies our understanding of gender's impact on political attitudes.
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How this classification was reachedexpand
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".