School-Aged Children's Phonological Accuracy in Multisyllabic Words on a Whole-Word Metric
Bibliographic record
Abstract
Purpose: The purpose of this study is to examine differences in phonological accuracy in multisyllabic words (MSWs) on a whole-word metric, longitudinally and cross-sectionally, for elementary school-aged children with typical development (TD) and with history of protracted phonological development (PPD). Method: Three mismatch subtotals, Lexical influence, Word Structure, and segmental Features (forming a Whole Word total), were evaluated in 3 multivariate analyses: (a) a longitudinal comparison (n = 22), at age 5 and 8 years; (b) a cross-sectional comparison of 8- to 10-year-olds (n = 12 per group) with TD and with history of PPD; and (c) a comparison of the group with history of PPD (n = 12) with a larger 5-year-old group (n = 62). Results: Significant effect sizes (ηp2) found for mismatch totals were as follows: (a) moderate (Lexical, Structure) and large (Features) between ages 5 and 8 to 10 years, mismatch frequency decreasing developmentally, and (b) large between 8- to 10-year-olds with TD and with history of PPD (Structure, Features; minimal lexical influences), in favor of participants with TD. Mismatch frequencies were equivalent for 8- to 10-year-olds with history of PPD and 5-year-olds with TD. Classification accuracy in original subgroupings was 100% and 91% for 8- to 10-year-olds with TD and with history of PPD, respectively, and 86% for 5-year-olds with TD. Conclusion: Phonological accuracy in MSW production was differentiated for elementary school-aged children with TD and PPD, using a whole-word metric. To assist with the identification of children with ongoing PPD, the metric has the ability to detect weaknesses and track progress in global MSW phonological production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".