What do students in human resource management know about accent bias?
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
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.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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