Same script, different sway: ethnolinguistic accent hierarchies in hiring evaluations in southern Ontario
Bibliographic record
Abstract
Canadians are perceived worldwide as being welcoming to people of diverse backgrounds, partly due to the government’s adoption of official multiculturalism. Nevertheless, immigrants face substantial barriers to integration. The present study explores whether bias exists against job applicants with non-Canadian English accents in the province of Ontario and what variables influence listeners’ evaluations of candidates’ responses. Verbal guises of ‘good’ and ‘bad’ answers to job interview questions were recorded by British, Chinese, German, Indian, Jamaican, and Nigerian women, and Canadian women with at least one parent from these countries. Ninety-six human resources students from nine post-secondary institutions in southern Ontario rated (1) the content of job interview responses, whether candidates were (2) easy to understand and (3) desirable employees, and (4) determined what job they should be interviewed for. Quantitative analysis via conditional inference trees and random forests reveals accent discrimination. Local voices received significantly higher ratings than non-local voices on all parameters and were recommended for more managerial-level and customer-facing job interviews. These results indicate that linguistic assimilation is expected of immigrants and bias against accents can lead to inequitable access to employment, contra to multiculturalism. Consequently, we call for language to be added to Ontario’s grounds for discrimination.
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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 | 0.002 | 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 | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".