Personality Traits and Common Ingroup Identity: Support for Refugee Policies Among Host Members
Bibliographic record
Abstract
Türkiye, which has hosted the largest number of refugees in recent years, requires remedial intervention programs to facilitate adaptation and coexistence. The irony of harmony studies that guide these interventions seem incomplete due to limited sample characteristics and a lack of attention to personality traits. Hence, we aimed to explore relationships between personality traits, identification with common ingroup identity, and support for social policies toward refugees by sampling the advantaged majority and the disadvantaged largest minority in Türkiye across two correlational studies (Ntotal = 772). In Study 1, agreeableness, extraversion, openness, narcissism, and psychopathy were associated with support for positive social policies. On the other hand, neuroticism was linked with support for negative social policies. However, when identification with common ingroup identity was included in the model, the significance of personality traits in almost all models disappeared, indicating that only the prediction of identification with common ingroup identity remained. The findings of Study 2 replicated and extended the previous result by sampling disadvantaged group members. We suggest that it may be more effective to focus on intergroup variables rather than personality traits to strengthen support for refugee policies, as the overall findings pointed out.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".