Women With Mandarin Accent in the Canadian English-Speaking Hiring Context: Can Evaluations of Warmth Undermine Gender Equity?
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
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
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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