Students' attitudes toward their instructors' accents in L2 Spanish and French
Bibliographic record
Abstract
The controversy about language instructors’ accent (i.e., the manner of pronunciation) has mainly targeted the perceptions and attitudes of learners of English as a foreign and second language (ESL/EFL). Some studies have consistently shown a tendency for learners to favour a native-speaking accent or being able to speak like a native speaker (Butler, 2007; Derwing, 2003). However, less is known about this topic in Romance language learning. The current study analyzes the attitudes and preferences learners of two Romance languages reported on how their instructors pronounced the target languages. The study also examined students’ attitudes toward their instructors’ accent on their own pronunciation and comprehension of the second language (L2). The participants were 20 third-year learners of Spanish as a foreign language (SFL); and 20 third-year learners of French as a second language (FSL) at a post-secondary institution in Canada. The data were collected through an attitudinal questionnaire (quantitative data) and a semi-structured interview (qualitative data). It was predicted that students would prefer an instructor with a native accent over an instructor with a non-native accent because of a facilitative effect on their pronunciation and comprehension of the L2. Results showed that both clusters of language learners (Spanish and French) favoured an instructor with a native accent and also showed the belief that the instructor’s native accent has a positive effect on their L2 pronunciation, but not on their L2 comprehension. Qualitative results suggested what strengths and limitations students believe each type of instructors’ accent offers for the language classroom.
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".