Identifying Linguistic Markers of French-Speaking Teenagers With Developmental Language Disorder: Which Tasks Matter?
Bibliographic record
Abstract
PURPOSE: This research aimed to identify reliable tasks discriminating French-speaking adolescents with developmental language disorder (DLD) from their peers with typical language (TL) and to assess which linguistic domains represent areas of particular weakness in DLD. Unlike English, morphosyntax has not been identified as a special area of weakness when compared with lexicosemantics in French preschoolers with DLD. Since there is evidence that subject-verb number agreement is consolidated in later childhood, one might expect morphosyntax to be a particular weakness and marker of French DLD only in (pre)adolescence. METHOD: = 12.2 years). Using robust statistics that are less affected by outliers, we selected the most discriminating subtasks between our groups, calculated their optimal cutoff score, and derived diagnostic accuracy statistics. We combined these subtasks in a multivariable model to identify which subtasks contributed the most to the identification of DLD. RESULTS: Seven subtasks were selected as discriminating between our groups, and three showed outstanding diagnostic accuracy: Recalling Sentences, a multiword task assessing lexicosemantic skills, and a subject-verb number agreement production task. When combined, we found that the latter contributed the most to our multivariable model. CONCLUSION: This study provides evidence that the most relevant markers to identify DLD in French teenagers are tasks assessing lexicosemantics and morphosyntactic domains, and that morphosyntax should be considered an important area of weakness in French-speaking teenagers with DLD. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.21753932.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".