Korean Language and Ethnicity in the United States: Views From Within and Across
Bibliographic record
Abstract
The current study, by focusing on a group of university‐level learners of Korean in the United States, explores the various ways in which Korean American students position themselves in and around the language and ethnicity. Over the course of 3 years, data for this study were collected through several methods, such as formal and informal interviews, participant observation in class meetings, and collection of students' work samples and other documents as a part of the participatory action research model. Through the diasporic life stories of Korean Americans, this study demonstrates how different notions of ethnicity restrict or create possibilities of constructing and negotiating ethnic identities. This study sheds light on the complex link between language and ethnicity by highlighting how different views of ethnicity are interrelated with language learning experiences and ethnic identities. It demonstrates how a dichotomous view of ethnicity as either primordial or instrumental limits possibilities of negotiation and construction of ethnic identities by positioning Korean American students into an either/or position. It highlights that a view of ethnicity as a continuum, as an alternative to the dichotomy, opens up space for the negotiation and construction of new ethnicities. This study draws attention to the need to pay attention to ethnicity in language education. Educators need to encourage learners to develop perspectives that acknowledge and recognize the role of ethnicity in the language learning process.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".