The Image of Gender and Political Leadership
Bibliographic record
Abstract
Abstract This is the first multi-country, factorial experiment on candidate gender designed to avoid social desirability bias and provide a real-world measure of the importance of gender via direct quantitative contrasts with party effect size (the experimental control, which was statistically significant in all cases). The eight countries: Canada in Alberta and Quebec, Chile, Costa Rica, England, Israel, Sweden, Uruguay, and the United States in California and Texas, are established presidential and parliamentary democracies that jointly offer variance on incorporation of women in government, policy agenda, electoral rules, and party system. Young-adult participants come from highly diverse socioeconomic backgrounds in all cases. Political science and psychology literatures are the basis of a multi-dimensional framework about how context molds mental templates of leadership, yielding eleven hypotheses. The 2×2×2 experimental factors, treatments (a lengthy candidate speech with partisan jargon and buzz words), field implementation, and ANOVA techniques used for analysis are outlined in detail. Resident in-country experts who implemented the experiment interpret findings against key country-specific historic and current events in separate country chapters, followed by a chapter providing a meta-analysis of all hypotheses across cases. Though many broad and case specific conclusions can be drawn, the main finding is that traditional leadership images (leaders are men) appear only where defense dominates the political agenda. Otherwise, in diverse contexts, women candidates are accepted as leaders by the participants, indicating young adults’ approval of women’s ability to hold diverse posts, win votes, and manage stereotypically masculine policy areas.
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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 | 0.004 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".