Something to Prove? Manhood Threats Increase Political Aggression Among Liberal Men
Bibliographic record
Abstract
Manhood is a precarious state that men seek to prove through the performance of masculine behaviors—including, at times, acts of aggression. Although correlational work has demonstrated a link between chronic masculine insecurity and political aggression (i.e., support for policies and candidates that communicate toughness and strength), experimental work on the topic is sparse. Existing studies also provide little insight into which men—liberal or conservative—are most likely to engage in political aggression after threats to their masculinity. The present work thus examines the effects of masculinity threat on liberal and conservative males’ tendency toward political aggression. We exposed liberal and conservative men to various masculinity threats, providing them with feminine feedback about their personality traits (Experiment 1), having them paint their nails (Experiment 2), and leading them to believe that they were physically weak (Experiment 3). Across experiments, and contrary to our initial expectations, threat increased liberal—but not conservative—men’s preference for a wide range of aggressive political policies and behaviors (e.g., the death penalty, bombing an enemy country). Integrative data analysis (IDA) reveals significant heterogeneity in the influence of different threats on liberal men’s political aggression—with the most effective being intimations of physical weakness. A multiverse analysis suggests that these findings are robust across a range of reasonable data-treatment and modeling choices. Possible sources of liberal men’s heightened sensitivity to manhood threat 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".