Evaluating the effects of chronological age and sentence duration on degree of perceived foreign accent
Bibliographic record
Abstract
Immigrants' age of arrival (AOA) in a country where a second language (L2) must be learned has consistently been shown to affect the degree of perceived L2 foreign accent. Although the effect of AOA appears strong, AOA is typically correlated with other variables that might influence degree of foreign accent. This study examined the pronunciation of English by native Italian immigrants to Canada who differed in AOA. As in previous research, those who arrived as young adults (late learners) were somewhat older at the time of testing, and produced somewhat longer English sentences, than those who arrived in Canada when they were children (early learners). The results of Experiment 1 showed that the greater chronological age of early than late learners was not responsible for the late learners' stronger foreign accents. Experiment 2 suggested that the late learners' longer L2 sentences were not responsible for observed early–late foreign accent differences. A principle components analysis revealed that variation in AOA and language use, but not chronological age or sentence duration, accounted for a significant amount of variance in the foreign accent ratings. The findings of the study were interpreted to mean that AOA effects on foreign accent are due to the development of the native language phonetic system rather than to maturational constraints on L2 speech learning.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 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".