Why do they code-switch? Examining code-switching, use of vernacular, and linguistic insecurity in a minority French-speaking context of Canada
Bibliographic record
Abstract
This paper examines the reasons why young French-English bilingual speakers of Vancouver (Canada) code-switch to English when speaking in French. It also discusses how the use of code-switching might be related to the absence of a French vernacular variety, which in turn is associated with a feeling of linguistic insecurity. The study is based on classroom observations and interviews with 31 grade-11 teenagers who attend a French-language school in Vancouver. A qualitative content analysis of the data revealed that these youth code-switch for three main reasons: lexical, conversational, and social. These motivations for code-switching are divided into nine types of code-switches. All these switches form a communicative strategy and constitute a resource that the participants use to support their peers, participate in activities underway, maintain a conversation in French, and connect with their peers. The findings also indicate that English is a dominant language for most participants, and that supporting the development of a strong vernacular variety in French might encourage them to speak French with their friends and support their feeling of linguistic security.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".