What do students in human resource management know about accent bias?
Bibliographic record
Abstract
For many second language (L2) speakers, including immigrants, speaking with an L2 accent can be a source of unfair or biased treatment in many workplace contexts. However, apart from research on language learners, there is currently little knowledge as to what the general public, and especially members of professional communities, know about accent and accent bias. Our goal in this study was to examine the intuitive understanding of accent and accent bias by university students in human resource (HR) management as future gatekeepers to gainful employment. We interviewed 14 students across two four-year university HR programs in Canada asking the students about their prior experience with accent bias and exploring their understanding of the broader construct of accent through thematic interview coding. The students reported multiple examples of accent bias, demonstrating a nuanced understanding of accent, where they characterized accent bias as an unconscious phenomenon, highlighted its experiential component, expressed sensitivity to different linguistic sources of accent, emphasized the role of a listener in L2 communication, and generally showed flexibility and tolerance toward accented L2 speech. We discuss these findings in light of prior work on accent awareness and highlight the importance of dedicated accent-focused training for HR professionals.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".