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Record W4319317329 · doi:10.1177/00084298221149695

The many facets of negative public opinion towards Muslims

2023· article· en· W4319317329 on OpenAlexaffvenueabout
Jacob Legault‐Leclair, Sarah Wilkins‐Laflamme

Bibliographic record

VenueStudies in Religion/Sciences Religieuses · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSecularismIslamophobiaPublic opinionImmigrationAffect (linguistics)State (computer science)Political scienceVariety (cybernetics)IslamSocial psychologySociologyPsychologyLawPoliticsGeography

Abstract

fetched live from OpenAlex

Few studies have delved empirically into the various factors driving Islamophobia and negative attitudes towards Muslims, or the various forms they can take among individuals. To what extent does state secularism (or laïcité) policy, such as Bill 21 in Quebec, affect attitudes towards Muslims among the general public? What are the various dimensions of attitudes towards Muslims that can be measured in recent years? Using 2011–2019 Canadian Election Study data, the authors do not find a large impact of state secularism policy on public opinion towards Muslims, nor a strong dislike of organized religion explaining all negative attitudes towards Muslims in Quebec and in the rest of Canada. Instead, they find a wide variety of negative attitudes towards Muslims: some respondents specifically targeted Muslims with their discomfort and dislike, while others showed dislike towards Muslims tied to wider xenophobic attitudes towards racial minorities, immigrants, and other minority and vulnerable groups in society.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.209
GPT teacher head0.485
Teacher spread0.276 · 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

Citations3
Published2023
Admission routes3
Has abstractyes

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