Evaluating causes of foreign accent in English sentences spoken by native speakers of Italian differing in age of arrival (AOA) in Canada
Bibliographic record
Abstract
This study evaluated potential causes of foreign accent (FA) by including native Italian (NI) speakers with a later age of arrival (AOA) in Canada than in previous studies. Three NI groups (n=18 each) differing in AOA (means=10, 18, and 26 years) participated. Listeners used a 9-point scale to rate sentences produced by the three NI groups and native English controls. The ratings obtained for all four groups differed significantly. The stronger foreign accents of the AOA-18 than AOA-10 group might be attributed to the passing of a critical period, or to stronger cross-language interference by more robust Italian phonetic categories. The difference might also be attributed to differences in language use. This is because the AOA-10 and AOA-18 groups (but not the AOA-18 and AOA-26 groups) differed significantly in percentage of English and Italian use, length of residence in Canada, and years of education in Canada. None of these explanations will apparently explain the stronger FAs of the AOA-26 than AOA-18 group. The difference between these groups might be attributed to cognitive aging [Hakuta et al., Appl. Psycholinguistics (in press)], which results in gradually less successful second-language acquisition across the adult life span. [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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".