When those fleeing the war are blue-eyed and blond: The effects of message content and social identity on blatant dehumanization in four nations
Bibliographic record
Abstract
Varying behaviours and attitudes towards those who experience the same devastating event are increasingly becoming the focus of criticism. Open expressions of these distinctions based on group membership, such as Kelly Cobiella's statement on NBC about refugees who fled Russia's invasion of Ukraine, " These are not refugees from Syria; these are refugees from neighbouring Ukraine ", have raised the question of the social psychological antecedents of these varying attitudes. This research examines how refugees' social identity (ingroup vs. outgroup) and the given reason for their fleeing from a regional war (fear vs. human rights violations) affect the blatant dehumanization of refugees by receiving country communities in four different countries ( N total = 1274). In Study 1, we found that Turks in Türkiye showed higher dehumanization toward Syrian refugees (outgroup members compared to Turkmen refugees) and toward those portrayed as fleeing the war due to fear (vs. human rights violations). Study 2, which focused on Germans' attitudes toward Ukrainian and Afghan refugees, showed that dehumanization was negatively associated with the perception of ingroup similarity. In Study 3, with a Spanish sample, we found that ethnic outgroup refugees (Syrians) were more dehumanized than ethnic ingroup refugees (Ukrainians). Similarly, Study 4, which sampled British participants and focused on the same ingroup and outgroup, found that ethnic outgroup refugees were more dehumanized than ethnic ingroup refugees. We discuss the consisted findings in four countries that there is more dehumanization towards members of groups that are less similar to participants from the perspective of the social identity approach.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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; a candidate call from one teacher head, 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".