Fairness of using different English accents: The effect of shared L1s in listening tasks of the Duolingo English test
Bibliographic record
Abstract
This study aimed to answer an ongoing validity question related to the use of nonstandard English accents in international tests of English proficiency and associated issues of test fairness. More specifically, we examined (1) the extent to which different or shared English accents had an impact on listeners’ performances on the Duolingo listening tests and (2) the extent to which different English accents affected listeners’ performances on two different task types. Speakers from four interlanguage English accent varieties (Chinese, Spanish, Indian English [Hindi], and Korean) produced speech samples for “yes/no” vocabulary and dictation Duolingo listening tasks. Listeners who spoke with these same four English accents were then recruited to take the Duolingo listening test items. Results suggested that there is a shared first language (L1) benefit effect overall, with comparable test scores between shared-L1 and inner-circle L1 accents, and no significant differences in listeners’ listening performance scores across highly intelligible accent varieties. No task type effect was found. The findings provide guidance to better understand fairness, equality, and practicality of designing and administering high-stakes English tests targeting a diversity of accents.
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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 | 0.058 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".