A child-specific compensatory mechanism in the acquisition of English /s/
Bibliographic record
Abstract
This study examines corpus data involving word-initial [sV] productions from 79 children aged 2–5 (Edwards & Beckman 2008) in comparison with a corpus of word-initial [sV] syllables produced by 13 adults. We quantified target-like /s/ production using spectral moment analysis on the frication portion (high center of gravity, low SD, and low skewness). In adults, we found that higher vowels (low F1 after normalization) were associated with more target-like /s/ productions, likely reflecting a tighter constriction. In children, older subjects produced more target-like outputs overall. However, unlike adults, children’s outputs before low vowels were more target-like, regardless of age. This is unexpected given the articulatory challenges of producing /s/ in low vowel contexts. Further investigation found that high F1 (low vowels) was associated with louder /s/ (relative to V) and more encroachment of sibilant noise on the following vowel (high harmonics-to-noise ratio). This finding suggests that young children may be increasing airflow during /s/ production to compensate for a less tight constriction when the jaw must lower for the following vowel. Thus, children may adopt a more accessible mechanism, different from adults, to compensate for their immature lingual gestures, possibly in an attempt to maximize phonological contrasts in word-initial position.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".