Exploring the ideational explanation for pro-immigrant sentiment: evidence from a South Korean survey
Bibliographic record
Abstract
Abstract A consistent finding in the public opinion literature shows that individuals who attain higher levels of education are more likely to express pro-immigrant attitudes. The ideational hypothesis suggests that ideas learned during formal education drive this empirical relationship. In this article, we develop this hypothesis further by asking, "What types of ideas socialize pro-immigrant attitudes?" We argue that exposure to social theories during higher education promotes social inclusivity and tolerance, leading to positive views toward immigrants. This article draws theoretical insights from attitudinal-based theories of immigrant sentiment to construct a mediation model linking ideas from the classroom to attitudes toward immigrants. Using original data from a population-based survey in South Korea, we examine the relationship between respondents’ prior enrollment in different academic courses and their attitudes toward immigrants. We measure exposure to social theories as enrollment in social science and arts & humanities and find that only social science courses are positively associated with pro-immigrant attitudes. We also examine whether enrollment exhibits indirect effects via previously identified attitudinal determinants of immigrant sentiment. Results from our mediation analysis show that enrollment in social science courses is associated with stronger cosmopolitan views and negatively correlates with isolationist attitudes. In contrast, we find that enrollments in courses unrelated to social theories, like math & science and engineering, are not statistically significant predictors of immigrant attitudes. We interpret our results as observational evidence consistent with ideational-based explanations for pro-immigrant attitudes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".