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Record W4309588921 · doi:10.3389/fpsyg.2022.973603

Cultural similarity predicts social inclusion of Muslims in Canada: A vignette-based experimental survey

2022· article· en· W4309588921 on OpenAlexaffabout
Hajra Tahir, Saba Safdar

Bibliographic record

VenueFrontiers in Psychology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsResentmentVignetteAcculturationPsychologySocial psychologySocial exclusionSocial identity theorySimilarity (geometry)Social groupSociologyEthnic group

Abstract

fetched live from OpenAlex

Based on acculturation psychology and intergroup emotions theory, the current experimental study assessed the effects of Muslims' perceived acculturation strategies by the majority group on social exclusion of Muslims in Canada, and to what extent religious resentment mediated the relationship between Muslims' perceived acculturation strategies and social exclusion. The experimental study used a vignette-based approach. This model was examined among 190 non-Muslim Canadians. Results showed that when Muslims were viewed as assimilated in Canadian society, social exclusion of Muslims and religious resentment toward Muslims decreased. Furthermore, religious resentment mediated the association between Muslims' perceived acculturation strategies and social exclusion only when Muslims were perceived as assimilated. Our findings suggest that Canadian majority-group members indicated positive attitude toward Muslims when they were identified as assimilated in Canadian society. Results are discussed in terms of implications for future studies and intergroup relations.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.350
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

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