Bibliographic record
Abstract
Understanding foreign-accented speech presents challenges for listeners. Three experiments tested learning of a foreign-accented vowel shift and its application during on-line speech processing. Native Quebec French and English speeches were compared using a visual world eye-tracking task. The French talker pronounced /i/ as /ɪ/ before all consonants except voiced fricatives. On critical trials, participants viewed pictures of an unaccented /i/ word (e.g., bees) and accented /i/ word (e.g., beet, pronounced [bt] by the French talker), then heard one talker say the target word. On French-talker trials, listeners should rule out the competitor because it does not share a vowel with the target, reducing competition. However, fixation measures showed comparable competition on “bees” trials and increased competition for the French talker on “beet” trials, indicating difficulty in applying knowledge of the accent. Performance on bees trials improved in Exp2-3, where stimulus variability was reduced by presenting only critical words with /t/ codas. On beet trials, performance improved most on Exp3, in which the unaccented counterpart to the accented word (e.g., bit, for beet) never appeared. The results suggest reducing linguistic variability is helpful in learning the accent, but lexical competition must also be reduced for successful processing of accented words.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".