Second Language Learners’ Attitudes Towards French Varieties: The Roles of Learning Experiences and Social Networks
Bibliographic record
Abstract
People often believe that certain language varieties are more prestigious than others (e.g., Kircher, 2014; Zhang & Hu, 2008), which can cause speech from perceived substandard varieties to trigger biases and inform social judgements of the speaker (Giles & Billings, 2004). These language-centered biases likely develop from classroom or cultural experience (Giles et al., 1974), but it is largely unknown what types of language experience and exposure might mitigate language biases, especially for second language (L2) learners engaged in classroom language learning. This study’s goal was to extend the limited knowledge on the effects of experience on L2 learners’ language-centered biases by focusing on L2 French learners’ attitudes towards different French varieties. \n \nParticipants included 106 L2 French learners from various proficiency levels engaged in L2 French learning in Montreal, a city characterized by negative attitudes towards speakers of Quebec French. Participants rated two audios recorded by native speakers from France in a listening comprehension task, with one of the two speakers introduced as a speaker of Quebec French. They described their language learning experience, filled out a French social network questionnaire, and completed a French proficiency test. Results showed that participants engaged in reverse linguistic stereotyping, preferring to speak like one speaker significantly more than the other, based on the speaker’s assumed identity, not actual speech. Speech ratings were also largely associated with participants’ 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".