Language and Racial Attitudes toward French Varieties in a Second Language Learning Context
Bibliographic record
Abstract
Language is a powerful social force that does more than merely communicate content. Language learners can show preference for certain varieties over others due to linguistic stereotyping and the awareness that accents can lead to an array of social and professional disadvantages. This study explores the hierarchization of different varieties of French within Canada and the interplay of variety and race. A modified matched-guise test was used to gather attitudinal data from 94 participants undertaking Frenchlanguage courses at the University of British Columbia in Vancouver. The goals of this study were to unpack the perceived hierarchization of five different varieties of French (from Moncton, Quebec City, Vancouver [a non-native speaker], Abidjan, and Paris) and to identify a possible correlation between the evaluation of varieties of French and the racialization of speakers. Participants evaluated speakers on four dimensions: status, solidarity, understandability, and general perspective. The findings revealed a clear hierarchy based on attitudes. For most statements, the participants evaluated speakers of Quebec and European French more favourably. Interestingly, they also evaluated the non-native speaker of French more positively than the speakers of African and Acadian French. However, for each variety of French, the voices associated with Black speakers were evaluated more positively than those associated with White speakers.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".