Testing the effects of political rhetoric towards muslims as a facilitator and barrier for intergroup contact
Bibliographic record
Abstract
Intergroup contact fosters positive social relations, and politicians often use rhetoric to shape intergroup attitudes. However, the impact of political rhetoric on future intergroup contact remains unexplored. This paper addresses this gap by examining how rhetoric influences contact readiness towards Muslims. We conducted two experiments in which non-Muslim participants were randomly assigned to one of three conditions: exposure to positive political rhetoric about Muslims, negative rhetoric, or a control condition. In Study 1, exposure to negative rhetoric did not significantly affect contact readiness. In contrast, positive rhetoric led to more positive perceptions of future contact, with higher intergroup trust and lower intergroup anxiety. Study 2, using a higher-powered sample, also showed positive rhetoric increased positive contact perceptions but only linked to intergroup trust. It also found positive rhetoric led to greater intentions for future contact. Negative rhetoric continued to show no direct effect on contact readiness but had conditional effects; it predicted higher intergroup anxiety and less positive contact perceptions among individuals with lower social dominance orientation. Overall, findings indicate political rhetoric, serving as a form of vicarious intergroup contact, can influence public willingness for intergroup interactions.
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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 | 0.003 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".