The construction of linguistic identities in talk about food among Tibetan heritage language learners
Bibliographic record
Abstract
This ethnographic article examines the interactional processes through which Tibetan-Canadian heritage language learners agentively construct their linguistic identities in everyday family interactions. Because food is centrally involved in family routines, as well as broader cultural practices and the articulation of individual tastes, talk about food provides a site for analyzing negotiations of individual agency amid expressions of shared identity. Drawing from previous language socialisation scholarship that approaches family meal times as a resource for constructing identity, this paper analyzes children’s conversations at meal-times, during food preparation, and about food preferences. Through interactional analysis of twelve months of longitudinal video ethnography in two Tibetan-Canadian families, I found that, in these activities centered on food, children used multilingual and multimodal resources to negotiate authority over their cultural and linguistic knowledge. Specifically, children agentively displayed their knowledge of Tibetan language forms, elaborated on their capacities to participate in cultural activities, and instructed parents about their individual preferences. By analyzing these interactional displays of knowledge through the concepts of agency and co-operative action, I argue that children construct their identities as speakers of Tibetan, despite the dominance of English in their language repertoires.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".