Evaluating the effects of bilingual dominance and language mode on overall degree of foreign accent
Bibliographic record
Abstract
This study evaluated the influence of bilingual dominance and language mode on overall degree of perceived foreign accent. Three groups of Italian–English bilinguals (n=12 each) were selected according to their ratio of self-rated English/Italian proficiency: English-dominant, balanced, or Italian-dominant. Language mode was manipulated by having participants repeat English phrases before and after similar Italian phrases (E1, E2) and then intermixed with Italian phrases (E3). Native English (NE) listeners rated four English phrases spoken by the bilinguals and 12 age-matched NE controls using a scale that ranged from 1 (strong foreign accent) to 9 (no foreign accent). We hypothesized that if switching into the native language (here, Italian) adversely affects pronunciation of the second language (English), the third repetitions of the English phrases (E3) should be more strongly foreign-accented than the first repetitions (E1). The foreign accent ratings decreased significantly in the following order: NE > English-dominant > balanced > Italian-dominant. That is, all three bilingual groups had detectable foreign accents, and strength of accent depended on bilingual dominance. The language mode effect was significant only for one of the four phrases examined, perhaps because it (mozzarella cheese) has distinctly different phonetic renditions in English and Italian. [Work supported by NIH.]
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".