Who should be admitted? Conjoint analysis of South Korean attitudes toward immigrants
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
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.
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) | 0.001 | 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 it