From Gender Gap to Gender Gaps: Bringing Nonbinary People into Political Behavior Research
Bibliographic record
Abstract
The gender gap in voting is one of the most well-documented findings in political behavior across democracies. However, gender gap research has traditionally assumed that everyone is either a man or a woman. How do nonbinary people differ from men and women in their party identification and voting behavior? We answer this question using data from the 2021 Canadian Election Study online panel. Nonbinary people are much less likely to identify with and vote Liberal or Conservative and much more likely to identify with and vote for the social democratic New Democratic Party (NDP) than both men and women. Many of these gaps persist even when restricting the analysis to LGBTQ respondents, adjusting for demographic variables that predict nonbinary identity, and adjusting for issue attitudes. Nonbinary people’s distinctiveness from men and women suggests that researchers need to incorporate nonbinary people into studies of political behavior.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".