“You’re a <i>Juksing</i> ”: Examining Cantonese–English Code-Switching as an Index of Identity
Bibliographic record
Abstract
Code-switching, the spontaneous switching from one language to another, shows unique structural and functional patterns in different bilingual communities. Though historically viewed as negative, it has been documented as an acceptable way of speaking in certain contexts, namely multilingual communities. We investigated the implications of code-switching on bilinguals’ language attitudes and identities in Toronto, a distinctly multilingual and multicultural metropolis. Twelve Cantonese–English bilinguals participated in a semi-structured interview discussing their code-switching and language attitudes. Interviews were then evaluated using a critical realist framework and analysed via first and second cycle coding. Code-switching elicited mixed emotions: It was a source of pride, but also a reminder of weak Cantonese language skills due to others’ metalinguistic comments and judgments. Participants’ code-switching indexed them as juksings, labelling them as Chinese individuals born and raised overseas, de-authenticating their Chinese group membership. Results are discussed with regard to ethnic identity and intra-group communication.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".