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Record W4411391207 · doi:10.1002/casp.70112

Cognitive Liberalisation Through a Different Lens: Intergroup Contact Attenuates the Relationship Between Intolerance of Uncertainty and Intergroup Bias Across Three Contexts

2025· article· en· W4411391207 on OpenAlexaff
Deborah Shulman, Richard J. Crisp, Rose Meleady, Gordon Hodson

Bibliographic record

VenueJournal of Community & Applied Social Psychology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsBrock University
FundersLeverhulme Trust
KeywordsPsychologyCognitionLiberalizationSocial psychologyCognitive biasIn-group favoritismEconomicsPsychiatry

Abstract

fetched live from OpenAlex

ABSTRACT Researchers in the field of intergroup contact recently proposed that contact can broaden the mind, a process referred to as cognitive liberalisation. Under the right conditions, contact can increase flexible and creative thinking, as well as encourage the adoption of less rigid worldviews. The current research takes a novel approach by exploring whether contact can also “liberalise” people from the need to rely on intergroup bias to manage discomfort with uncertainty. We draw on Uncertainty‐Identity Theory to argue that intergroup contact can ameliorate the regulatory function of intergroup bias for reducing subjective uncertainty. Using three large‐scale Project Implicit datasets ( N total = 25,046), we tested whether contact moderates the relationship between intolerance of uncertainty and intergroup bias and found that intolerance of uncertainty was associated with intergroup bias among people who do not experience contact with gay, transgender, or disabled people, but this association was generally weaker or non‐significant among people who experience contact. These results add to growing support for the liberalising impact of intergroup contact by elucidating a new benefit: Reduced reliance on intergroup bias as a means of managing subjective uncertainty.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.192
GPT teacher head0.451
Teacher spread0.259 · 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.

Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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