The Mosque Next Door: How the Visibility of Mosques Influences Support for the Far-Right and Anti-Immigration Policies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract As racial-ethnic and religious minorities have grown in Western societies, so too has the electoral success of nativist and far-right political parties. These parties commonly mobilize against Muslim minority groups, often targeting Islamic symbols such as mosques. This raises a key question: does the presence of mosques in local communities influence citizens’ vote choice? To answer this question, we analyze aggregate voting patterns in Swiss municipalities between 2007 and 2023. This includes data on voting returns from five elections and six anti-immigration popular initiatives. We augment these data with original spatial data that locates mosques in Switzerland, categorizing them as either a visible or non-visible feature of the built environment. Using coarsened exact matching (CEM), we estimate the causal effect of prominent, visible mosques on citizens’ voting patterns. Results indicate that a visible mosque in a municipality increases support for the far-right by approximately 3% points across elections. Similarly, a visible mosque increases support for popular initiatives targeting Muslims and other migrants by 3–5% points. By contrast, non-visible mosques have no significant effects on voting in popular initiatives or far-right party support. These findings highlight how politically salient features of the built environment shape voting patterns.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| 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 it