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Record W2607454299 · doi:10.1037/pac0000240

Supportive contact and LGBT collective action: The moderating role of membership in specific groups.

2017· article· en· W2607454299 on OpenAlexaff
Pirathat Techakesari, Lisa Droogendyk, Stephen C. Wright, Winnifred R. Louis, Fiona Kate Barlow

Bibliographic record

VenuePeace and Conflict Journal of Peace Psychology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsSimon Fraser UniversitySheridan College
Fundersnot available
KeywordsSocial psychologyCollective actionPsychologyAction (physics)Political sciencePoliticsLaw

Abstract

fetched live from OpenAlex

A growing body of literature suggests that positive cross-group contact between members of advantaged and disadvantaged groups can undermine disadvantaged group members’ collective action engagement. It has also been proposed that supportive contact (i.e., positive contact with advantaged group members who express explicit support for social change) may be a special form of contact that might increase, rather than reduce collective action engagement among disadvantaged group members. In the present research, we tested this proposition by asking Gay (N = 96) and Lesbian (N = 100) Australians to recall a previous positive interaction with a heterosexual friend who was either very supportive or somewhat supportive of Lesbian, Gay, Bisexual, and Transgendered (LGBT) rights (i.e., equal marriage, adoption, and surrogacy rights). Results revealed that the effect of supportive contact on collective action intentions depended on the participants’ specific disadvantaged group membership. For Gay men, those who recalled supportive contact reported greater collective action intentions. The opposite pattern emerged for Lesbian women. These findings suggest that supportive contact has the power to enhance or undermine collective action intentions among the disadvantaged. Which of these occurs appears to depend on the specific disadvantaged group to which one belongs. The psychological underpinnings of these effects, the theoretical implications, and future research directions are discussed.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.094
GPT teacher head0.407
Teacher spread0.313 · 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.

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

Citations22
Published2017
Admission routes1
Has abstractyes

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