Do L2 Speakers’ Assumptions About the Test Examiner Influence Their Speaking Anxiety in an Oral Exam? A Reverse Linguistic Stereotyping Study
Bibliographic record
Abstract
A speech may be perceived differently depending on what social information we draw from the speaker, be it correct or misguided (Burgers & Beukeboom, 2020; Edwards, 1999; Lambert et al., 1960; Niedzielski, 1999). The phenomenon where non-linguistic social information about the speaker (e.g., race, occupation, etc.) influences our actual experience with speech is called reverse linguistic stereotyping (RLS; Kang & Rubin, 2009). Although RLS has been well documented for its influence on speech perception (e.g., seeing an Asian face can render a speech less comprehensible and more accented), its broader impact on speakers is still less known. This study, therefore, examined how L2 speakers’ assumptions about examiners influence their speaking anxiety in an oral exam. Participants included 40 Mandarin-speaking international students in Montreal, who completed two English speaking tests delivered through video prompts. Each test featured a different examiner (Caucasian or South Asian), while the audio remained constant (Canadian English). Participants rated their speaking anxiety before and after each test and evaluated each examiner. Retrospective recall interviews were conducted with eight individuals who showed noticeable difference in pretest anxiety ratings across two examiners. Results revealed no significant difference in speaking anxiety in Test 1. However, a higher pretest anxiety was observed in Test 2 when the examiner appeared South Asian than Caucasian, possibly due to a shift in visual stimuli, which activated the stereotypical association between examiner’s race and linguistic ability. The findings highlighted the importance of creating a more inclusive environment in both language learning and assessment.
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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 | 0.001 | 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) | 0.002 | 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".