Candidate Gender, Behavioral Style, and Willingness to Vote
Bibliographic record
Abstract
This article explores the impact that women’s and men’s nonverbal forms of communication have on voters’ evaluations of political figures. The results indicate that nonverbal cues employed by female and male politicians during political speeches trigger both leadership and gender stereotypes. Furthermore, these behaviors produce different reactions among male and female viewers. Our results indicate that while female politicians are not generally stereotyped as being less agentic (strong leaders, aggressive, tough, confident, or decisive) than men, when they are observed using agonic (assertive, expressive, or choppy) hand movements, their assessments drop. Men demonstrating the same behavior see their leadership assessments improve. Nonverbal cues have little effect on gender-based stereotypes linked to communal qualities such as being caring, sociable, emotional, sensitive, and family oriented, but do impact willingness to vote for a candidate. Women are more likely to receive votes particularly from male respondents if they are calm and contained. Male candidates are more likely to be supported by both women and men when they communicate using assertive nonverbal behaviors.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".