Phonological Skills in English Language Learners
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine the English phonological skills of English language learners (ELLs) over 5 time points. METHOD: Sound class accuracy, whole-word accuracy, percentage of occurrence of phonological patterns, and sociolinguistic correlational analyses were investigated in 19 ELLs ranging in age from 5;0 (years;months) to 7;6. RESULTS: Accuracy across all samples was over 90% for all sound classes except fricatives and increased for all sound classes across time. Whole-word accuracy was high and increased across time. With the exception of cluster reduction, stopping, and final consonant deletion, the frequency of occurrence for phonological patterns was less than or equal to 5% at every time point. Sociolinguistic variables such as age of arrival, age of exposure, and age were significantly related to phonological skills. CONCLUSIONS: The results were consistent with the hypotheses outlined in Flege's (1995) speech learning model in that the phonological skills of ELLs increased over time and as a function of age of arrival and time. Thus, speech-language pathologists (SLPs) also should expect phonological skills in ELLs to increase over time, as is the case in monolingual children. SLPs can use the longitudinal and connected-speech results of this study to interpret their assessments of the phonological skills of ELLs.
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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 | 0.000 |
| 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".