Negotiating hybridity: transnational reconstruction of migrant subjectivity in Koreatown, Los Angeles
Bibliographic record
Abstract
Transnationalism has emerged as a key factor in altering immigrant ethnic enclaves by networking them with global flows of capital and labor. A quintessential example is Koreatown in Los Angeles, often portrayed as the ‘overseas Korean capital.’ The area has experienced rapid transition since the mid-1990s that is related to a huge influx of South Korean transnational investment and, concomitantly, migrants of various backgrounds. This study investigates the resulting transformation of the built environment, residential composition, and social relations in Koreatown. Of particular interest are the ways in which Korean and other transnational migrants flexibly alter their identities in terms of the situations in which they exist. Semi-structured and informal interviews with key informants were conducted, focusing on autobiographic narrations related to the discursive structure of their identities. Information from mainstream and Korean-American newspapers and previous academic work also are central to interpreting the qualitative data. We argue that, in contrast to the common view, Los Angeles's Koreatown is a highly multicultural, heterogeneous space. Therefore, it is suggested that this area should be reconsidered as a hybrid, rather than homogeneous, space where intra- and interethnic identities are flexibly reproduced, contested, and combined in the course of localized global interactions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".