Full Transfer and Segmental Emergence in the L2 Acquisition of Phonology: A Case Study
Bibliographic record
Abstract
In this paper, we discuss a child Kazakh speaker’s acquisition of English as her second language. In particular, we focus on this child’s development of the English segments |f, v, θ, ð, ɹ, ʃ, ʧ|, which are not part of the Kazakh phonological inventory of consonants. We begin with a longitudinal description of the patterns that the child displayed through her acquisition of each of these segments. The data reveal patterns that range from extremely rapid to rather slow and progressive acquisition. The data also reveal patterns that were unexpected at first, for example, the slow development of |ʧ| in syllable onsets, an affricate that occurs as a contextual allophone in syllable onsets in Kazakh. We analyze these patterns through the Phonological Interference hypothesis, which was recently extended into the Feature Redistribution and Recombination hypothesis. These models predict the transfer into the L2 of all of the relevant phonological features present within the learner’s first language and their recombination to represent segments present in the L2. We also discuss contexts where feature-based approaches to L2 acquisition fail to capture the full range of observations. In all such contexts, we show that the facts are modulated by phonetic characteristics of the speech sounds present in either the child’s L1 or her L2.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".