When Accent Does Not Match Expectations: A Dynamic Perspective of L2 Speaker Evaluations in a French Interview Context
Bibliographic record
Abstract
According to expectation violation theory, job applicants can be upgraded or downgraded during an interview when their accent does not match employers’ speech expectations. Focusing on the employment of second language French job candidates in Québec, this study explored this issue dynamically in terms of how expectations may impact the trajectory of interview evaluations. Participants included 60 Québec French raters and 6 female job candidates applying to a waitress or pizza cook position, presented through their resumes as either first (L1) or second (L2) language French speakers. Each speaker’s interview audios were presented to raters in expectancy-congruent and expectancy-incongruent scenarios. Raters first provided resume-based employability assessments, then two more evaluations throughout a typical sequence of interview questions. The congruent and incongruent scenarios revealed similar evaluation patterns, where the L2 French cook applicant’s employability improved after initially being downgraded. Implications are discussed regarding listeners’ readjustment of their perceptions following first-impression biases.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".