Critical Ethnography of a Multilingual and Multicultural Korean Language Classroom: Discourses on Identity, Investment and Korean-ness
Bibliographic record
Abstract
Following critical/post-structural perspectives in conducting ethnographic research on the political dimension of language learning, this study examines language learners’ identity and investment in a post-secondary Korean language classroom in Canada. First, this study explores the ways in which Korean-ness is produced through the curriculum, how an instructor’s linguistic and teaching practices in the Korean language classroom function to include some students and exclude others, and how the students on the periphery cope with their marginalization. I argue that peripheral students’ coping strategies are strongly tied to their investment into certain aspects of Korean language and culture, as well as their desire to gain symbolic resources in the Korean language. Second, my study examines the ways in which Korean heritage language learners (re)negotiate their hyphenated Korean Canadian identities by looking at three different discourse sites - Korean home, Korean church, and Canadian schools - and how their hyphenated identities are connected with their investment in maintaining their heritage language. The data for this study includes classroom observations, semi-structured interviews, bi-weekly written journals and focus group interviews. By adopting critical discourse analysis (CDA) as a means of analyzing the data, this study shows that language learners’ race, ethnicity and gender are salient parts of their identities, and thus impact their learning experiences to varying degrees and levels. My research findings also suggest that the ethnic identity capital that the heritage language learners embrace in relation to their perceptions of their native speech community as well as its status, is intertwined with the maintenance of their heritage language. Pedagogical implications from this study enable educators to equally empower students from diverse backgrounds, and help them to be sensitive to the relations between ideologies and power in the language classroom. Central to these pedagogical implications is that it is the role of the teacher to adequately capitalize on the multilingual and multicultural practices that each student brings to the language classroom, and to identify the social and cultural voices present in the class.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".