Understanding attitudes towards refugees: The roles of group size and racial background cues
Bibliographic record
Abstract
This study builds on previous work that shows that perceived lack of control and racial prejudice can induce more negative attitudes toward refugees. Lack of control is particularly salient in immigration when flows are not easily regulated by the state and when the public perceives a large number of refugees, especially in comparison to the status quo. Furthermore, when refugees are perceived as being more culturally distant from the host society, we expect prejudice to dampen support for refugees. In this study, we test a new method for manipulating group size (to manipulate feelings of control) and racial cues using randomly assigned emojis (to manipulate cultural distance and group size). Compared to general information about the number of refugee claimants in Canada, we test three different size cues presented as proportions of the Canadians population (1 in 400, 10 in 4,000, 100 in 40,000), and three racial cue manipulations (no image, yellow default emojis, and brown tone emojis). We expect prior attitudes about diversity will moderate these effects.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.082 | 0.021 |
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".