Second Language Learners’ Attitudes Toward French Varieties: The Roles of Learning Experience and Social Networks
Bibliographic record
Abstract
Abstract People often believe that some language varieties are more prestigious than others, which can trigger speech‐centered biases and inform social judgments of the speaker. However, it is largely unknown what types of language experience and exposure might mitigate language biases, especially for second language (L2) learners. The goal of this study was to investigate this issue by focusing on L2 French learners’ attitudes toward European and Quebec varieties of French. L2 French learners in Montreal (N = 106) rated 2 audios recorded by native speakers from France in a listening comprehension task, with 1 of the 2 speakers introduced as a speaker of Quebec French. The learners described their language learning experience, filled out a French social network questionnaire, and completed a French proficiency test. Results revealed some evidence of reverse linguistic stereotyping, with learners preferring to speak like one speaker significantly more than the other, based on the speaker's assumed identity, not actual speech. Four of the 6 speaker ratings were also associated with participants’ oral proficiency scores, social network density, and positive experiences in Quebec. Findings have implications for the use of speech models in L2 teaching and for the mitigation of language‐centered biases in L2 classrooms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".