Who should be admitted? Conjoint analysis of South Korean attitudes toward immigrants
Bibliographic record
Abstract
South Korea is slowly but steadily becoming a country of immigrants. In 1998, there were barely 300,000 foreign residents in South Korea. As of 2018, there were more than 2.3 million. The immigrant population has yet to reach 5% of the total population, but it is predicted to rise significantly in the years to come. Despite the increase in newcomers, it is not well understood who native South Koreans prefer as immigrants and why. Are immigrant attitudes motivated by co-ethnic solidarity, or are they primarily based on economic and sociotropic concerns? To isolate attitudes on these crucial questions, this research uses a conjoint experiment that simultaneously tests the influence of seven immigrant attributes in generating support for admission. Our results show that broad sociotropic concerns largely drive attitudes towards immigrants in South Korea, but an immigrant’s origin also matters. Prospective newcomers from culturally similar and higher-status countries who can speak Korean and have clear plans to work are most preferred. The research findings will be relevant to the comparative study of immigration attitudes, as well as to researchers interested in the specifics of the South Korean case.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".