Rethinking Belongingness in Korea: Transnational Migration, "Migrant Marriages" and the Politics of Multiculturalism
Bibliographic record
Abstract
In 2006, a 30-year-old American football player, Hines won the Most Valuable Player (MVP) award in the Super Bowl. Mr. Ward happened to have a Korean mother and an African-American father. Virtually overnight, Mr. Ward became a veritable sensation in his birthplace, which was more than a bit ironic given the long and very intense discrimination by Koreans against mixed-blood (honhyol) children and their mothers. irony of the situation, as Mary Lee points out, was no accident. The social buzz over Hines Ward, Lee writes, can be read as an attempt to achieve some sort of expedited closure on the issue of long-standing discrimination against interracial people.1 Even more, this social buzz?which was much more like a chainsaw?marked, for the first time, a countrywide recognition that South Korea was in the midst of a potentially profound social transformation, with equally profound political, economic and cultural ramifications. basis for this social transformation is clear, namely, the rapid growth of transnational migration/immigration to South Korea. This story is now fairly well known: beginning in the late 1980s, there has been a constant and constantly growing inflow of foreign newcomers (primarily unskilled workers) to South Korea. In the space of less than two decades, from 1990 to 2007, the number of foreign residents2 in South Korea grew from just under 50,000 to over one million?a 2,000 percent increase. (By
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.055 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".