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Record W4404287854 · doi:10.1002/casp.70011

Do Positive and Negative Intergroup Contact Create Shifts in Ingroup and Outgroup Attitudes Over Time: A Three‐Wave Longitudinal Study Testing Alternative Mediation Models

2024· article· en· W4404287854 on OpenAlexaff
Sabahat Çiğdem Bağci, Sami Çoksan, Abbas Türnüklü, Mustafa Tercan

Bibliographic record

VenueJournal of Community & Applied Social Psychology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsWestern University
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsOutgroupIngroups and outgroupsMediationContact theoryPsychologySocial psychologyLongitudinal studyLongitudinal dataMathematicsStatisticsSociologyDemographySocial science

Abstract

fetched live from OpenAlex

ABSTRACT The current study investigated how contact experiences may be associated with attitudes towards the ingroup and the outgroup using a three‐wave longitudinal study. We assessed Turkish native children's contact with Syrian refugees ( N = 487, M age = 10.60, SD age = 0.90) and explored relationships between initial contact and later ingroup and outgroup attitudes testing alternative mediation models. We also examined whether negative contact with outgroup members may directly or indirectly predict more positive ingroup attitudes. Findings demonstrated that positive contact was associated with both reduced ingroup positivity and increased outgroup positivity over time. However, unlike the traditionally suggested mediational pathway in contact‐deprovincialization literature, initial positive contact (T1) was associated with less positive ingroup attitudes (T3) through more positive outgroup attitudes at T2. There was no evidence for the role of negative intergroup contact on ingroup or outgroup attitudes. Findings are discussed within the broader scope of contact theory and the recently growing deprovincialization literature. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement .

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.130
GPT teacher head0.421
Teacher spread0.292 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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