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Record W3199187471 · doi:10.1108/ijcma-10-2020-0177

Do you hear my accent? How nonnative English speakers experience conflictual conversations in the workplace

2021· article· en· W3199187471 on OpenAlexaff
Regina Kim, Jimena Y. Ramirez‐Marin, Kevin Tasa

Bibliographic record

VenueInternational Journal of Conflict Management · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsYork University
Fundersnot available
KeywordsPsychologyStereotype (UML)Stereotype threatSocial psychologyOriginalityStress (linguistics)Group conflictPerceptionLinguistics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.096
GPT teacher head0.343
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2021
Admission routes1
Has abstractyes

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