Non-participation and the stability of Dominant Language Constellation in contextual shifts: the case of a Hong Kong multilingual student
Bibliographic record
Abstract
This study explores how a multilingual Hong Kong student exercised agency to maintain a stable Dominant Language Constellation (DLC) amidst significant shifts in sociolinguistic contexts. Employing an intrinsic case study approach, the study collected data through interviews and written documents, which were then analysed using thematic analysis. The findings revealed that the multilingual student strategically managed his language use by compartmentalising Cantonese, Putonghua, and English into distinct domains, a practice he maintained consistently across different environments, including Hong Kong, Beijing, and Montreal. Key sources of his agency were identified, such as his self-conception as an introverted individual, parental influence, and the demands of his educational contexts. His strategic non-participation in social interactions helped him avoid linguistic discomfort and maintain psychological stability. This study contributes to the field of multilingualism by illustrating how agency can be manifested through deliberate non-participation and strategic language use, offering new insights into the dynamic interplay between individual agency and sociolinguistic environments.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".