Bibliographic record
Abstract
In 2018, the South Korean government denied refugee status to all but two of the almost 500 Yemenis who, fleeing civil war in their home country, had arrived earlier in the year on the resort island of Jeju. This decision was made in the context of a short-lived but intense public backlash, even though the overall number of refugees has remained consistently low. Three years later, nearly 400 Afghans were evacuated to South Korea with government support and little controversy. What explains these different patterns of refugee politicization in South Korea? I argue that the 2018 episode of anti-refugee activism in South Korea does not follow the typical script in immigration politics which pits "natives" against "outsiders"; rather, it is a reflection of internal political divisions. In this article, I focus on political framing contests involving governmental and non-governmental actors that draw upon prior rhetorical frames of political mobilization which had developed in a broader context of state-building, development, and democratization. The 2018 Jeju "crisis" was partially a reaction against state-led multiculturalism ( damunhwa ), which had gained momentum since the 2000s. It also signalled a political backlash against previous decades of social and political movements that framed labour rights, migrant workers' rights, and other minority rights as a necessary expansion of human rights befitting a responsible "advanced nation." At the same time, the varied responses to the arrival of Yemenis in 2018 and Afghans in 2021 show that the coherence and resonance of competing political frames during key moments can help explain the type and degree of political mobilization on refugee policy. Furthermore, these comparative case studies show that South Korean attitudes toward refugees have not settled into stable political-economic coalitions and remain contested and in flux.
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.
How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.004 |
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; both teacher heads agree on what is shown here.
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".