MétaCan
Menu
Back to cohort
Record W4411225961 · doi:10.1016/j.jesp.2025.104780

Testing the effects of political rhetoric towards muslims as a facilitator and barrier for intergroup contact

2025· article· en· W4411225961 on OpenAlexafffund
John Shayegh, Becky L. Choma

Bibliographic record

VenueJournal of Experimental Social Psychology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Toronto
FundersUK Research and InnovationToronto Metropolitan University
KeywordsFacilitatorRhetoricPsychologyPoliticsSocial psychologyPolitical scienceLinguisticsLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.425
Teacher spread0.388 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Experimental Social PsychologySame topicSocial and Intergroup PsychologyFrench-language works237,207