Accent Bias and Perceptions of Professional Competence in England
Bibliographic record
Abstract
Unequal outcomes in professional hiring for individuals from less privileged backgrounds have been widely reported in England. Although accent is one of the most salient signals of such a background, its role in unequal professional outcomes remains underexamined. This paper reports on a large-scale study of contemporary attitudes to accents in England. A large representative sample ( N = 848) of the population in England judged the interview performance and perceived hirability of “candidates” for a trainee solicitor position at a corporate law firm. Candidates were native speakers of one of five English accents stratified by region, ethnicity, and class. The results suggest persistent patterns of bias against certain accents in England, particularly Southern working-class varieties, though moderated by factors such as listener age, content of speech, and listeners’ psychological predispositions. We discuss the role that the observed bias may play in perpetuating social inequality in England and encourage further research on the relationship between accent and social mobility.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".