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Record W2517870257 · doi:10.1017/s0008423916000561

Religious Symbols, Multiculturalism and Policy Attitudes

2016· article· en· W2517870257 on OpenAlexaffabout
Dietlind Stolle, Allison Harell, Stuart Soroka, Jessica E. Behnke

Bibliographic record

VenueCanadian Journal of Political Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsToronto Public HealthUniversité du Québec à MontréalMcGill University
Fundersnot available
KeywordsMulticulturalismDiversity (politics)Ethnic groupPolitical sciencePublic opinionTurkishReligiositySalientPublic policyCultural diversitySociologyPublic relationsGender studiesLawPolitics

Abstract

fetched live from OpenAlex

Abstract Multicultural policy is an increasingly salient, and contested, topic in both academic and public debate about how to manage increasing ethnic diversity. In spite of the longstanding commitment to multiculturalism policy in Canada, however, we have only a partial understanding of public attitudes on this issue. Current research tends to look at general attitudes regarding diversity and accommodation–rarely at attitudes towards specific multicultural policies. We seek to (partly) fill this gap. In particular, we focus on how support for multiculturalism policy varies across benefit types (for example, financial and other) and the ethnicity/religiosity of recipient groups. Using a unique survey experiment conducted within the 2011 Canadian Election Study (CES), we examine how ethnic origin (Portuguese vs. Turkish) and religious symbols (absence and presence of the hijab) influence support for funding of ethno-religious group activities and their access to public spaces. We also explore whether citizens’ general attitudes toward cultural diversity moderate this effect. Results provide important information about the state of Canadian public opinion on multiculturalism, and more general evidence about the nature, authenticity and limits of public support for this policy.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.323
Teacher spread0.307 · 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 designTheoretical or conceptual
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

Citations15
Published2016
Admission routes2
Has abstractyes

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