Women With Mandarin Accent in the Canadian English-Speaking Hiring Context: Can Evaluations of Warmth Undermine Gender Equity?
Bibliographic record
Abstract
Although many workers speak with a non-native English accent, our understanding of this phenomenon is limited because prior work predominantly focused on men. This overlooks whether the biases women experience due to their accent manifests differently. To address this omission, we use an intersectional lens to examine how non-native accents associated with more gender-traditional countries may affect women's hiring outcomes. We argue that the bias women with these accents face is subtle due to an association of non-native (vs. native) accents with perceptions of women's warmth (whereas there are no such effects for men) and consequently higher perceptions of hireability. Yet we posit that the indirect effect on hireability occurs within feminine, but not masculine, industries, which ultimately undermines equity by pushing women with these non-native accents into lower pay and prestige occupations. We found support for our hypotheses in three vignette-based experiments conducted in Canada using a Mandarin accent. Managers and decision-makers need to be aware of the insidious bias women with these non-native accents experience because it may not be immediately apparent that an association of accent with higher ratings of warmth may undermine women at work. Additional online materials for this article are available on PWQ's website at https://journals.sagepub.com/doi/suppl/10.1177/03616843231165475
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".