Full Transfer and Segmental Emergence in the L2 Acquisition of Phonology: A Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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