Nonnative accent and the perceived grammaticality of spoken grammar forms
Bibliographic record
Abstract
Abstract Scholars advocate for more classroom attention to be paid to spoken grammar which deviates from commonly taught rules of writing. However, these recommendations have not considered potential barriers that learners may encounter when using spoken grammar with L1 speakers. We investigate one such challenge: the effect of learners’ accents and degree of accentedness on how their use of these forms is subjectively perceived by L1 speakers. Ten non-expert raters rated the grammatical acceptability of four frequent spoken grammar forms, read out by 15 speakers (10 L1 Tagalog, 5 L1 English) rated as having heavy, moderate, or no accents. A one-way ANOVA revealed a significant effect of accent on grammaticality scores. Post-hoc tests showed a strong correlation between accent and perceived grammaticality, with more accented speakers scoring significantly lower on grammaticality. The discussion considers implications for spoken grammar teaching, and future research on the relationship between accent and perceived grammaticality.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".