The application of bilingual phonological learning models to early second language development
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The goal of this chapter is to review how bilingual phonological learning models can be applied to understanding early second language development among children in kindergarten. Specifically, we review Best’s Perceptual Assimilation Model, Flege’s Speech Learning Model, Kuhl’s Language Magnet Model, and Willem van Leussen & Escudero’s Second Language Phonetic Model. We present results from a study of 25 children who were attending kindergarten in French and who spoke Tagalog and English at home. The children’s consonant productions in a word naming task were transcribed and analysed. Although the results did not align with a single model, it was useful conceptually to contrast “shared” versus “unshared” phonemes as the “unshared” phonemes were produced with lower accuracy overall.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it