The many facets of negative public opinion towards Muslims
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".