Making Women Visible: How Gender Quotas Shape Global Attitudes toward Women in Politics
Bibliographic record
Abstract
Abstract Since the 1990s, gender quotas have been celebrated for improving women’s equality. Yet their cross-national and longitudinal impact on attitudes toward female politicians and the mechanism through which this process occurs are not well understood. Using multilevel modeling on 87 nations, we examine how different types of quotas, with varied features and levels of strength, shape beliefs about women in politics. We give particular attention to the mechanism of visibility created by quotas in impacting attitudes. Results suggest that unlike quotas with features facilitating low visibility (i.e., weak quotas), those producing high visibility (i.e., robust quotas) significantly impact public approval of women in politics. However, the direction of this effect varies by quota type. Social context also matters. Robust quota effects—both positive and negative—are especially pronounced in democracies but are insignificant in nondemocracies. Limited differences by gender (men versus women) emerge. Theoretical and policy implications are discussed.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".