Linguism’s Impact on Careers: Identity Formation, Mobility, Flexibility in Multinational Corporation
Bibliographic record
Abstract
This panel symposium explores how linguicism—discrimination or favouritism based on language—shapes career identity, mobility, and flexibility in multinational corporations (MNCs). Despite the growing focus on diversity and inclusion in global organisations, linguistic discrimination remains underexplored, particularly its profound impact on employees' professional trajectories and identity formation. Through the lenses of linguistic hegemony, language-based microaggressions, linguistic discrimination, and linguistic flexibility, the session will examine how language practices influence career experiences in MNCs. By engaging a diverse group of scholars and practitioners, this symposium aims to combine theoretical, empirical, and practical insights to illuminate the ways linguistic practices create barriers or opportunities for employees. The discussion will highlight actionable strategies to foster equitable linguistic policies and practices that support inclusivity and enable all employees to thrive, irrespective of their linguistic backgrounds. By addressing these challenges, the symposium aims to contribute to rethinking organisational dynamics and advancing the discourse on diversity and inclusion in global workplaces.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| 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".