Racializing the problem of and solution to foreign accent in business
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it