Gendered Perceptions and the Costs of Political Toxicity: Experimental Evidence from Politicians and Citizens in Four Democracies
Bibliographic record
Abstract
Politicians frequently face toxic behaviors. We argue that these behaviors impose a double burden on women, who may not only face higher exposure to toxicity, but experience attacks that they and others understand to be motivated by prejudice and designed to push them out of office. Using large-scale image-based conjoint experiments in the United States, Denmark, Belgium, and Chile, we demonstrate that both politicians themselves and citizens regard messages targeting women politicians as more toxic than otherwise equivalent messages targeting men. This perception intensifies when messages mention gender or come from perpetrators who are men. A second experiment to investigate the mechanisms shows that hostile behaviors toward women are more frequently understood as driven by prejudice and attempts to remove women from politics. These findings highlight the importance of understanding how perceptions of perpetrators’ motives affect the severity of political toxicity, and provide insights into the gendered effects of political hostility.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.013 |
| 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".