Racializing the problem of and solution to foreign accent in business
Bibliographic record
Abstract
Abstract Given the desire to attract skilled immigrants to English-speaking countries in the Global North, business environments in these nations may see the increased presence of workers who speak English with a foreign accent. While organizations may tout this linguistic diversity, there is a concern that a foreign accent interferes with successful business communication. This apparent issue can result in a lack of employment opportunities for foreign-accented professionals and has also created a rise in private accent reduction programs that seek to improve the employability of these professionals. What is understated or even omitted in the discussion of these trends is the impact of racialization. First, the (lack of) employability of foreign-accented workers may be determined by a set of racial hierarchies in which some bodies are perceived as better for work than others. Furthermore, the notion that simply reducing an accent increases one’s employability ignores racialized power structures that truly prevent the employment of certain immigrants. Through the lens of raciolinguistic ideologies, which look at the intermingling of language and race, this article explores the above issues by arguing that foreign accent discrimination and accent reduction are indeed racialized and thus perpetuate the inequality experienced by immigrant professionals in business.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".