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Has Intergroup Contact Delivered?

2025· article· en· W4409417581 on OpenAlex
Matt Lowe

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnual Review of Economics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyEconomicsComputer science

Abstract

fetched live from OpenAlex

Intergroup contact is arguably the prejudice reduction intervention with the most existing empirical support. However, recent meta-analyses of experimental contact interventions find signs of publication and reporting biases. To mitigate such bias, I carry out a meta-analysis of 41 preregistered contact experiments, considering only treatment effects on preregistered primary outcomes. I find that ( a ) the average effects of intergroup contact are smaller than indicated by previous findings, at roughly one-tenth of a standard deviation; ( b ) the subset of in-person interventions that satisfy Allport's four desirable scope conditions (e.g., common goals) are no more effective; and ( c ) generalization is limited: Contact is more effective at changing behavior and attitudes toward people met than toward the outgroup as a whole. I offer suggestions for how researchers might make progress on the problem of generalization through careful measurement of its extent and the consideration of moderating factors beyond those emphasized by Allport.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.032
GPT teacher head0.346
Teacher spread0.314 · 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