The gender gap in voter turnout: An artefact of men’s over-reporting in survey research?
Bibliographic record
Abstract
Is there a gender gap in voting? Most cross-national survey research on gender inequalities in voter turnout finds that men have a higher probability to vote than women. Yet, some studies using validated turnout data shed some doubt on this finding. We revisit the question of a gender gap in voting using official records. In more detail, we compare the gender gap in turnout between survey data and official electoral figures across 73 elections. Our results highlight that in surveys, men still report higher turnout in most countries. However, official electoral figures reveal contrasting trends: across countries, women are, on average, more likely to vote. We also test two explanations for this difference in turnout between official figures and surveys: (1) men over-report voting more than women and (2) the survey samples of men and women are different. We find some, albeit very moderate, evidence for the first explanation and no support for the second explanation. All in all, our research nevertheless suggests that scholars should be careful in using surveys to detect gender differences in voting.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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; a candidate call from one teacher head, 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".