What Determines Voting Behaviors of Muslim Minorities in Europe: Muslim Identity or Left‐Right Ideology?
Bibliographic record
Abstract
Voting is key to political integration of immigrant‐background minorities, but what determines their voting preferences remains unclear. Moreover, dual‐citizen minorities can vote differently in their country of residence and origin. Using a representative survey of Turkish‐Muslim minorities in two cities in Belgium ( N = 447, M_ age = 36.3), we asked whether left‐right ideology or religious identity predicted their voting in their country of residence and origin, besides typical predictors of right‐wing voting (i.e., efficacy, deprivation, and authoritarianism). Authoritarianism, low political efficacy, and high deprivation predicted voting for right‐wing parties in Turkey, whereas the latter two, surprisingly, predicted voting for the left in Belgium. Latent class analyses of their religious practices distinguished “moderate” versus “strict” Muslims. While “strict” Muslims voted for right‐wing parties in Turkey, ideology did not predict their voting. Conversely, in Belgium, while Muslim identity did not predict their voting, ideology did. Analyzing their combined effects, “moderate” Muslims voted based on their ideology—right‐leaning voting for the right, whereas “strict” Muslims voted according to their interests as a disadvantaged minority in Belgium—thus voting for the left—or as a devout Muslim in Turkey—thus voting for the right. Our results elucidate processes underlying the voting behaviors of European‐Muslim minorities.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".