Are Personality Traits Related to Politicians’ Positions on Immigration?
Bibliographic record
Abstract
The political debates spurred on by rapidly growing immigrant populations in many countries have resulted in an extensive, and growing, scholarship that seeks to explain citizens’ attitudes toward immigration. Yet, there is surprisingly an absence of research regarding the factors that correlate with political elites’ positions on immigration. This study therefore seeks to address an important scholarly gap by exploring the factors that help to explain politicians’ positions on immigration. Specifically, this study is inspired by the growing research into personality that underlines psychological traits as being important determinants for a wide variety of citizens’ sociopolitical attitudes, including attitudes towards immigration. Using data from the 2010 Swedish Candidate Survey, our findings highlight that candidates’ personality traits are related to their immigration attitudes. Specifically, extraversion and openness are shown to have positive relationships with attitudes towards immigrants. Furthermore, while the political context of the candidates can moderate some of the relationships between personality and attitudes toward immigration, our results show that personality traits are associated with immigration attitudes independent of political considerations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".