Exploring attitudes towards French, English, and code-switching in Manitoba (Canada)
Bibliographic record
Abstract
This study contributes to the understanding of attitudes towards monolingual and code-switched varieties by examining the perceptions of 95 bilinguals towards Manitoban French, Canadian English and code-switching in Manitoba, a Canadian province where French is a minority language with official federal status. By means of a matched-guise test, we explore French-English bilinguals’ social evaluations of the three linguistic varieties and examine how these social evaluations vary according to participant characteristics (i.e. age, gender, mother tongue, origin, and sociocultural identity). In our experiment, participants listened to a speaker using Manitoban French, Canadian English and code-switching and rated each guise on several solidarity and status traits. Results from the cumulative link mixed effects models reveal that French and English are rated similarly for status. Overall, both French and English elicit feelings of attachment, but a preference towards French emerges among participants born in Manitoba. Code-switching is rated lower than the monolingual varieties in most status and solidarity traits, which indicates that our participants implicitly value linguistic purism. However, results also show that participants born in Manitoba and those with French as their mother tongue ascribe covert prestige to code-switching.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".