Accent Bias in Professional Evaluations: A Conceptual Replication Study in Brazil
Bibliographic record
Abstract
Abstract Evidence from Canada suggests that accent bias can be moderated by speakers’ demonstrated job-relevant performance and the prestige level of their occupation (Teló et al. 2022). In this study, we replicated Teló et al.’s (2022) work in Brazil. First language (L1) Brazilian Portuguese-speaking listeners rated audio recordings of L1 Brazilian Portuguese and L1 Spanish speakers along continua capturing one professional (competence), one experiential (treatment preference), and one linguistic (comprehensibility) dimension. Our findings challenge the notion of consistent bias, as listeners did not uniformly perceive L1 Brazilian Portuguese speakers as more competent and comprehensible than L1 Spanish speakers, and, in fact, generally preferred treatment provided by L1 Spanish speakers. Complex interactions provided a nuanced account of listeners’ evaluations, revealing, among other patterns, that demonstrated performance level and job prestige affected the evaluated dimensions differently depending on the speaker’s L1. This replication further expands the initial study by examining the role of four listener variables as predictors of speaker ratings. Greater listener familiarity with the context depicted in the script was associated with the assignment of higher ratings overall.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| 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".