Gendered Accent Penalty: An Informal Network Perspective on Employment Discrimination
Bibliographic record
Abstract
Does having a non-native accent have different effects for men and women’s chances of getting hired? Through two studies of hiring that include job applicants with native (North American) and non-native (Indian) accents, we draw on the stereotype content model to demonstrate that in recruitment and competence ratings, men with non-native accents are penalized more severely for their non-native accent compared to women. In Study 1, we found that women were not really penalized for their accents. We then replicated the findings of Study 1 in a second study with a managerial sample and tested informal network attractiveness as a key mediator in the relationship between male job applicant’s non-standard accent and their job suitability ratings. We found that informal network attractiveness mediates the relationship between male job applicant’s accent and job suitability ratings. The theoretical and practical implications of our research for scholarship on accent bias, intersectionality and hiring discrimination are discussed.
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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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".