Effects of the age of second language learning on the duration of first and second language sentences: The role of suppression
Bibliographic record
Abstract
The primary aim of this study was to account for the finding that late bilinguals produce longer English sentences than early bilinguals. In Experiment 1, Italians who immigrated to Canada either between the age of 2–13 years (“early bilinguals”) or 15–28 years (“late bilinguals”) repeated matched English and Italian sentences following an aural model. The early bilinguals produced shorter English than Italian sentences, whereas the late bilinguals showed the opposite pattern. The same countervailing pattern was evident in Experiment 2, where bilinguals shortened sentences by 20% when instructed to repeat sentences as rapidly as possible. Subgroups of bilinguals who reported using Italian oftenM=46% Italian use) but not seldom (M=8%) were found to have produced significantly longer English sentences than native English (NE) speakers did. The results were interpreted to mean that the late bilinguals produced longer English sentences than the early bilinguals because they needed to expend more resources to suppress their Italian subsystem than the early bilinguals. The perceptual effect of sentence duration was evaluated in Experiment 3, where pairs of English sentences differing in duration were presented to NE-speaking listeners for foreign accent ratings. A 10% shortening caused sentences spoken by late bilinguals to sound less foreign accented but it caused sentences spoken by early bilinguals to sound more foreign accented.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".