A multidimensional view of gradient change in velar acquisition in three-year-olds receiving phonological treatment
Bibliographic record
Abstract
The acquisition of phonemes does not occur in an "all or nothing" manner; instead, children gradually acquire dimensions of phonological knowledge. This gradual acquisition of phonemes is explored in the present study by comparing three types of measures taken from speech samples of three preschool-aged girls with a Speech Sound Disorder. The process of acquisition of velar stops was measured during 16 weeks of Cycles based speech treatment. Three types of measures were used to study the gradual acquisition of velar stops: acoustic analyses using voice onset time (VOT) for initial consonants and vowel duration for final consonants, speech adaptability using the Glaspey Dynamic Assessment of Phonology, and phonetic accuracy based on phonetic transcription. The children were assessed prior-to, after 8, and after 16 sessions of treatment based on a modified Cycles approach. At the onset of the study, the children had begun the process of acquiring velar stops. Differences on acoustic measures and speech adaptability measures were observed for velars that were not reflected in the phonetic transcription. The acoustic analyses and the speech adaptability measures were more sensitive and incremental in showing change over time when compared to phonetic transcription, with fewer ceiling and floor effects across the children. Although the individual profiles of gradient change were not simple, the acoustic and adaptability measures provided additional information regarding gradient change, and support our argument that a necessary approach is one that describes multiple dimensions of a child's phonological knowledge.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".