What is that I hear? An interdisciplinary review and research agenda for non‐native accents in the workplace
Bibliographic record
Abstract
Summary Speaking with a non‐native English accent at work is a prevalent global phenomenon. Yet, our understanding of the impact of having a non‐native accent at work is limited, in part because research on accents has been multidisciplinary, fragmented, and difficult for scholars to access and synthesize. To advance research on accents in the workplace, we provide an interdisciplinary and integrative review of research on non‐native accents drawing from the communications, social psychology, and organizational sciences literatures. First, we briefly review the dominant approaches taken in each literature. Second, we organize and integrate extant research findings using a 2 × 2 framework that incorporates the two main theoretical perspectives used to explain the effects of accents—stereotypes and processing fluency—and the two primary categories of workplace outcomes examined—interpersonal (i.e., others' evaluations of speakers with non‐native accents, such as hiring recommendations) and intrapersonal (i.e., non‐native‐accented speakers' own evaluations and experiences, such as sense of belonging). To facilitate future research, we end by articulating a research agenda including theoretical and methodological expansions related to the study of accents, identifying critical moderators, adopting an intersectional approach, and studying group‐level and potential positive effects of speaking with non‐native accents.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".