Late-Emerging Developmental Language Disorders in English-Speaking Monolinguals and English-Language Learners: A Longitudinal Perspective
Bibliographic record
Abstract
Research involving monolinguals has demonstrated that language impairment can be noticed in the early years and tends to persist into adolescence. More recently, research has begun to address the challenges of identifying and treating Developmental Language Disorders (DLD) in English Language Learners (ELLs). Developmental patterns of DLD are not necessarily consistent over time, and we hypothesized that some monolinguals and ELLs go "under the radar" in lower grades but their language difficulties become more pronounced in later years, as syntactic demands increase, hence "late-emerging DLD". This longitudinal study examined (a) the existence of late-emerging DLD in Grades 4-6 in English-speaking monolinguals and ELLs, and (b) the Grade 1 and 3 cognitive and language profiles that predict late-emerging DLD. This study involved monolinguals (n = 149), and ELLs (n = 402) coming from diverse home language backgrounds. Cognitive (working memory, phonological short-term memory, processing speed), language (vocabulary and syntax), and word reading skills were assessed annually from grades 1 to 6. Separate parallel analyses in the monolingual and ELL samples confirmed that late-emerging DLD exists in both groups. In comparison with their typically developing peers, late-emerging DLD can be identified as early as Grade 1 based on poorer performance on phonological awareness, naming speed, and working memory.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".