The Language Socialization and Identity Negotiations of Generation 1.5 Korean- Canadian University Students
Bibliographic record
Abstract
This article, based on a larger longitudinal multiple-case study of Generation 1.5 Korean-Canadians, explores two female students’ experiences in high school and then university. Foregrounding aspects of language socialization (Duff & Hornberger, 2008) and identity (Norton, 2000) in language-learning and use, the study examines the contextual factors involved in the students' language socialization in and through Korean and English. The findings reveal that through the complex interplay of their past, present, and future “imagined” experiences, the students were socialized into various beliefs and ideologies about language-learning and use, often necessitating the negotiation of investments in their identities in relation to Korean and English. Given the personal backgrounds of these students, coupled with the phenomena of globalization and transnationalism, we suggest that Canadian universities and Generation 1.5 students and their families pay more attention to the students’ linguistic, educational, and social backgrounds, affiliations, and trajectories by underscoring the advantages of bilingualism and biculturalism along with the importance of English for integration into Canadian society and international networks.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".