Mind Your Language: The Effects of Linguistic Ostracism on Interpersonal Work Behaviors
Bibliographic record
Abstract
Business and demographic trends are conflating to bring language issues at work to the forefront. Although language has an inherent capacity for creating interpersonal bonds, it can also serve as a means of exclusion. The construct of linguistic ostracism encapsulates this phenomenon. Drawing on ethnolinguistic identity theory, we identify how linguistic ostracism influences two interpersonal work behaviors: interpersonal citizenship and interpersonal deviance. We conduct a set of studies that uses multisource data, data across time, and data from three countries. Our results reveal that linguistic ostracism was associated with the enactment of lower interpersonal citizenship behaviors and higher interpersonal deviance behaviors. We find that disidentification served as a mechanism to explain why linguistic ostracism resulted in interpersonal citizenship behaviors and interpersonal deviance behaviors. Furthermore, linguistically ostracized employees with low (vs. high) social self-efficacy engage in fewer interpersonal citizenship behaviors and greater interpersonal deviance behaviors. We discuss theoretical implications associated with the phenomenon of linguistic ostracism and the implications for managers working in linguistically diverse organizations.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".