Assessing Accent Anxiety: A measure of Non-native English Speakers’ concerns about their Accents
Bibliographic record
Abstract
Non-native speakers (NNS) often experience anxiety due to challenges posed by their accented speech. Building on these insights, this paper introduces an instrument, the Accent Anxiety Scale (AAS), specifically designed to assess three sources of NNSs’ accent anxiety, including: (1) NNS’s apprehension about negative evaluations about themselves, personally, tied to their non-standard pronunciation (Fear of Negative Evaluation), (2) concerns about rejection from the native speaker community because of their "foreign" pronunciation (Fear of Intergroup Rejection), and (3) anxieties over potential communication hurdles attributed to their pronunciation (Intelligibility Concerns). We evaluated the psychometric robustness of the AAS by analyzing data from a total of 474 immigrant and international student NNSs at a western Canadian university. Study 1 (N = 203) employed exploratory factor analysis and correlational analysis, Study 2 (N = 153) employed confirmatory factor analysis and replicated validation in study 1, and Study 3 (N = 118) tested temporal consistency and provided further evidence validating the scale. Robust evidence emerges supporting the factor structure, reliability, and validity of the AAS. The findings not only support the use of the AAS in research, they also offer implications for pedagogical strategies aimed at alleviating NNSs’ accent anxiety.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".