Do you hear my accent? How nonnative English speakers experience conflictual conversations in the workplace
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the experiences of nonnative speakers in conflictual situations with native speakers in the workplace. In three studies, the authors examine whether nonnative speakers experience stereotype threat in workplace conflict situations with native speakers, whether stereotype threat is associated with certain conflict managing behaviors (e.g. yielding and avoiding) and the relationship between stereotype threat, satisfaction with conflict outcomes and processes, and objective conflict outcomes. Design/methodology/approach Studies 1 and 2 use critical incident recall methodology to examine nonnative speakers’ conflict behaviors and satisfaction with conflict outcomes. In Study 3, data were collected from a face-to-face simulation with a random-assignment design. Findings Findings suggest that nonnative speakers indeed experience heightened stereotype threat when interacting with native speakers in conflict situations and the experience of stereotype threat leads to less satisfaction with conflict outcomes, perceptions of goal attainment, as well as worse objective conflict outcomes. Originality/value The current study is one of the first studies to document the effects of accent stereotype threat on conflict behaviors and outcomes. More broadly, it contributes to the conflict studies literature by offering new insight into the effects and implications of stereotype threat on workplace conflict behaviors and outcomes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".