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Record W2791777567 · doi:10.1002/ejsp.2365

Positive and negative intergroup contact: Interaction not asymmetry

2018· article· en· W2791777567 on OpenAlexaff
Katrín Árnadóttir, Simon Lolliot, Rupert Brown, Miles Hewstone

Bibliographic record

VenueEuropean Journal of Social Psychology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyAsymmetrySocial psychologyContact theoryGeneralitySalience (neuroscience)Cognitive psychology

Abstract

fetched live from OpenAlex

Abstract This research reports a novel investigation into the comparative effects of positive and negative direct and extended intergroup contact on intergroup orientations. It tested the generality of the positive–negative asymmetry effect among majority (N = 357) and minority (N = 101) group members in Iceland. Little evidence of asymmetry was observed: the beneficial effects of positive contact were mostly as strong as the detrimental effects of negative contact, for both direct and extended contact. However, evidence was found for alternative interaction models in which positive contact buffers the negative effects of negative contact, and negative contact enhances the benefits of positive contact. These interaction effects were found only for direct contact and principally in the majority group, but were also found for the minority group, though more weakly. No interaction was observed for extended contact. It appeared that differential group salience elicited by positive and negative contact could partly contribute to the explanation of the observed effects, at least in the majority sample.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.039
GPT teacher head0.385
Teacher spread0.346 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations115
Published2018
Admission routes1
Has abstractyes

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