Are Personality Traits Related to Politicians’ Positions on Immigration?
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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