Facts versus feelings: Objective and subjective experiences of diversity differentially impact attitudes towards the European Union
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
This research used secondary data sources to examine how objective and subjective experiences of diversity and immigration are associated with voting and attitudes toward the European Union. Using objective measures of diversity and migration, England’s electorate regions with the most diversity and highest levels of projected migration had the lowest proportion of “Leave” voters in the 2016 Brexit vote (Study 1). Using subjective assessments of intergroup contact and immigration attitudes (Study 2), higher perceived immigrant population size was associated with greater perceived competition with immigrants and Euroscepticism, whereas intergroup contact had the opposite effect. Surprisingly, the explicit desire to reduce immigration was not associated with anti-EU attitudes. This research highlights the importance of combining objective and subjective measures of diversity and immigration in analyzing political motivations, as objective measures suggested immigration did not adversely affect Brexit votes (Study 1), whereas some subjective perceptions of immigration led to greater anti-EU attitudes.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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