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Record W2795588758 · doi:10.1017/s1755048317000748

Strange Bedfellows? Attitudes toward Minority and Majority Religious Symbols in the Public Sphere

2018· article· en· W2795588758 on OpenAlexafffundabout
Antoine Bilodeau, Luc Turgeon, Stephen White, Ailsa Henderson

Bibliographic record

VenuePolitics and Religion · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsCarleton UniversityConcordia UniversityUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of CanadaConcordia UniversityUniversité du Québec à Montréal
KeywordsOpposition (politics)ReligiosityPoliticsAlliancePublic spherePrejudice (legal term)FeelingSocial psychologyPolitical scienceMinority groupPublic opinionSociologyPsychologyEthnic groupLaw

Abstract

fetched live from OpenAlex

Abstract In this study, we contend that distinguishing individuals who support bans onminorityreligious symbols from those who want to banallreligious symbols improves our understanding of the roots of opposition to minority religious symbols in the public sphere. We hypothesize that both groups are likely driven by markedly different motivations and that opposition to the presence of minority religious symbols in the public sphere may be the result of an alliance between “strange bedfellows,” clusters of individuals whose political outlooks usually bring them to opposite sides of political debates. Drawing on a survey conducted in the province of Quebec (Canada), we find that while holding liberal values and low religiosity are key characteristics of those who would ban all religious symbols, feelings of cultural threat and generalized prejudice are central characteristics of those who would only restrict minority religious symbols. Negative attitudes specifically toward Muslims, however, also appear to motivate both groups.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.004
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.035
GPT teacher head0.341
Teacher spread0.306 · 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

Citations38
Published2018
Admission routes3
Has abstractyes

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