Code-Switching among Trilingual Montrealers: French, English and a Heritage Language
Bibliographic record
Abstract
'Ibis study focuses on the concept of code-switching, which can be defined as the verbal strategy by which multilingual speakers change linguistic code(s) within the same speech event as a sign of cultural solidarity or distance and as an act of cultural identity. The participants partaking in this study are three trilingual persons living and working in Montreal. They possess linguistic competencies in the following three languages: English, French and Greek. This study is of particular interest due to the complexity of the socio-political, ethnic, and linguistic context of the setting which entails bilinguality (English and French) according to Canada's language laws, while Quebec's provincial laws profess to only one official language (French). In addition, the anecdotal fact that heritage languages of certain minority groups are maintained and widely utilized by members of the specific group in Montreal adds to the unique context in which the trilingual participants were brought up, and that in which they currently live. This study consists of an analysis of the codes (and code-switches) used in the participants' audio-taped conversation, obtained as data with consent and later transcribed for analysis.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".