The Invisible Barrier: How Language Diversity Shapes Work and Career Experiences and Team Dynamics
Bibliographic record
Abstract
With increasing globalization, managing a diverse multilingual workforce, including one with Non-Native English Speaking (NNEs) employees, is becoming increasingly challenging for management. This symposium aims to contribute to our understanding of workplace experiences of NNEs and their individual and team outcomes across various organizational contexts. The collection of papers in the proposed symposium explores several critical areas, such as (a) how language diversity affects the development of the transactive memory system in multilingual teams with NNEs, (b) how the English-only mandated policies in MNCs impact the employment outcomes of Chinese, Italian, German, or Russian accented NNEs (c) under what conditions, and why, the intersection of race/ethnicity and language diversity (Native English Speaker and NNE status) impacts the hireability and advancement of Latinx employees, (d) how linguistic identity helps or impedes Chinese expatriates working with their Arab counterparts and other NNEs in United Arab Emirates, and (e) how NNEs with marginalized identities navigate stereotypes about their competence in high-status jobs. Further, by employing various theoretical frameworks and methodologies, the symposium enhances our understanding of how a relatively underexamined facet of diversity impacts team processes and individual employment outcomes. Ultimately, our goal with this symposium is two-fold. First, is to advance the scholarship on language diversity within the DEI arena, and second, to foster greater collaborations among researchers with diverse disciplinary training (e.g., in HR, OB, Careers, and IM) to broaden our understanding of language diversity to mitigate language-based biases and improve the work, team, and career experiences of NNEs. Keywords: Language Diversity, Non-native English accents, Workplace and Career Outcomes, Multilingual Teams’ Knowledge Sharing Practices Language Diversity and Transactive Memory System Foundations in Multilingual Teams Author: Mary E Zellmer-Bruhn; Carlson School of Management Author: Youjeong Song; University of Minnesota Author: Mary M. Maloney; Author: Henrik Bresman; INSEAD Lost In Pronunciation: English Mandates and Accent-Based Bias in Global Hiring Author: Catherine Summers; University of Michigan Author: Christy Zhou Koval; Michigan State University Author: Chad Vickers; University of Virginia Author: Melissa C. Thomas-Hunt; The Sound of Diversity: The Cost of Ethnic Accents on Hiring Decisions and Career Advancement Author: Megha Yadav; Indiana University South Bend Author: Romila Singh; University of Wisconsin-Milwaukee Adopting Acronym Names and Linguistic Identities: Understanding the Experiences of Expatriates Author: Juana Du; Royal Roads University Author: Debora Linehan; Royal Roads University The Hidden Intersection: Exploring Accentism and Status in the Workplace Author: Salome Opoku; Arizona State University Author: Milena Tekeste; New York University Abu Dhabi
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".