Korean-Canadian Children’s Bilingualism: Language Positions and Supporting Factors Within and Beyond a Multi-Generational Ethnic Church
Bibliographic record
Abstract
Adopting Bourdieu’s (1991) theoretical and analytic tools of field, habitus and capital, this year-long ethnographic case study examines the positions of the Korean and English languages at various levels within a multi-generational ethnic church. The research also identifies multiple levels of supporting factors of Korean-Canadian children’s bilingual development within and beyond this church. Data sources include classroom observations, interviews, curriculum materials, children’s artifacts, Korean government documents, as well as records of school meetings. In this study, the positions of Korean and English within the church are unveiled in the Korean and English ministries, which are closely linked to immigrant generations, and in the language use and socialization of children in the Grades 3 and 4 focus class. The positions of the languages within the church are influenced by the status of those languages beyond the church, demonstrating the close relationship between language and identity. This study also finds that the Korean language school in this church is a field in which the aims of the Korean government and Korean-Canadian immigrants intersect vis-à-vis heritage language education. For the Korean government, it is ultimately a field for strengthening national resources. For the church congregants, it is essentially for their heritage language and culture maintenance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".