The Effect of Women in Government on the Idealized Leader: A Comparative, Experimental Analysis
Bibliographic record
Abstract
Existing literature on the ideal traits of political officeholders demonstrates that voters tend to prefer leaders with classic masculine traits (e.g., confident, assertive, tough). However, much of this literature is over 10 years old and focused only on the United States; very little comparative work exists on this subject, and what does exist is over 20 years old (i.e., Williams & Best 1990). This paper reinvigorates and updates this debate on voter prejudice through an examination of whether the increased presence of women at all levels of the state has decreased the use of the masculine leader as the ideal in the minds of the voter. In other words, are women in government changing the voters’ expectations about the traits and behavior of the ideal leader? Using the results from an original experiment with over 600 participants performed in both the US and Canada, I demonstrate that the average voter has indeed changed some of their views on what the personality of a leader should be. However, contrary to the literature on the consequences of increased descriptive representation, I also find that there is little evidence that voters are changing their perceptions of women in general; the benefits of increased representation seem to be isolated to women leaders. Therefore, it appears that while the increased proportion of women in office has done wonders for the voters’ perceptions of female leaders, it may be doing little for the perception of women at large.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".