Assessing the contribution of second language experience and age of learning in Catalan/Spanish bilinguals’ perrception and production of English sounds.
Bibliographic record
Abstract
Previous research in immersion settings has shown that an early age of onset of second language (L2) learning, together with long-term exposure to the L2, are determinant factors for perceiving and producing L2 sounds accurately [e.g., Flege, MacKay, & Meador (1999)]. However, research in formal learning contexts has resulted in negative evidence for an early age of learning advantage [e.g., Garcia-Lecumberri & Gallardo (2003)] or in divergent experience effects (Cebrian, 2003, 2006). This study aimed to further examine the contribution of the factors of age of onset of L2 learning (AOL) and experience in a foreign language learning environment. Catalan/Spanish bilinguals studying English at university, with AOLs of 4 to 14 years and a minimum of 7 years of formal instruction, performed an AXB discrimination task, a picture narrative, and a delayed sentence repetition task. Results revealed that Catalan/Spanish bilinguals with somewhat longer exposure to English and an earlier AOL tended to discern English sounds at higher correct rates. By contrast, a great degree of variability was found across the bilinguals' extemporaneous and prompted production of English segments. Findings are discussed in terms of current models of L2 speech acquisition and their application to formal learning settings. [Work supported by postdoctoral fellowship from the Ministerio de Educacion y Ciencia and the FECYT (Spain).]
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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 | 0.001 | 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".