Examining language and racial attitudes in an L2 French learning context
Bibliographic record
Abstract
This is a quantitative study on language and racial attitudes among learners of French in the English-dominant context of British Columbia (Canada). Such attitudes matter because they can have an impact on students’ learning outcomes in and outside the classroom and they reflect linguistic and racial ideologies that exist in society. A verbal guise test was used to gather data from 83 participants who were taking intermediate and advanced French courses at the University of British Columbia (UBC) at the time of this study. Five varieties of French (from Montreal, Moncton, Vancouver, Abidjan and Paris) and two races (Asian and White) were included. The objectives were to investigate the effect of accent and race on the participants’ language attitudes. The results revealed a clear preference for European French and Quebec French, but these findings can be challenged by the participants’ difficulty in distinguishing among the different varieties. Interestingly, L2 French was evaluated more positively than the L1 varieties from Moncton and Abidjan – a finding that points to the importance of having exposure to non-standard accents in order to enhance positive attitudes. Finally, the participants also demonstrated a preference for the accents of Asians (compared with Whites) in evaluating spoken French.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".