MétaCan
Menu
Back to cohort
Record W4409752404 · doi:10.1002/casp.70101

Indirect Contact and Collective Action Among Disadvantaged Groups: A Multi‐Level Mini‐Meta‐Analysis

2025· article· en· W4409752404 on OpenAlexaff
Priscilla Lok‐chee Shum, Marisa L. Mylett, Ziv Levin, Stephen C. Wright, Agostino Mazziotta, Lisa Droogendyk, Lisa M. Bitacola

Bibliographic record

VenueJournal of Community & Applied Social Psychology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsSheridan CollegeSimon Fraser University
Fundersnot available
KeywordsMeta-analysisDisadvantagedCollective actionAction (physics)PsychologyPolitical scienceMedicinePhysics

Abstract

fetched live from OpenAlex

ABSTRACT It is well established that positive contact between members of different groups can reduce prejudice. However, there is also evidence that direct contact with advantaged group members can undermine disadvantaged group members' engagement in collective action. Also, considerable evidence shows that effective contact need not be direct. Mere knowledge of cross‐group friendships (extended contact) or observing positive contact (vicarious contact) can also reduce prejudice. This raises the question of whether these indirect forms of contact might also undermine collective action. We conducted a mini‐meta‐analysis of eight unpublished studies, including a range of intergroup contexts and samples, that measured indirect contact with advantaged group members and collective action among disadvantaged groups. We found a small but significant relationship that was consistently negative but varied in size depending on how indirect contact was measured. Contrary to expectation, more indirect contact predicted reductions in normative forms of collective action as strongly as radical forms.

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)0.016
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.032
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0100.034
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.182
GPT teacher head0.460
Teacher spread0.278 · 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 designMeta-analysis
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

Explore more

Same venueJournal of Community & Applied Social PsychologySame topicSocial and Intergroup PsychologyFrench-language works237,207