Morphosyntactic Development and Severe Parental Neglect in 4-Year-Old French-Speaking Children: ELLAN study
Bibliographic record
Abstract
Language is the most frequently compromised area of development in English-speaking neglected children, particularly the morphosyntactic component of language. This is very worrisome given its central role in academic success and social participation. No previous study has examined the morphosyntactic skills of French-speaking neglected children, despite the morphological richness of French. This study aimed to fill this gap. Forty-four neglected (mean age = 48.32 months, SD = 0.45) and 92 non-neglected (mean age = 48.07 months, SD = 0.24) French-speaking children participated. Measures of morphosyntactic skills were derived from a sample of spontaneous language collected during standardized semistructured play and analyzed using Systematic Analysis of Language Transcripts software (2012) . Four morphosyntactic indicators were compared using analyses of variance and Kolmogorov–Smirnov tests: the mean length of utterances (MLU), verbal inflections, word-level errors, and omission errors. The results indicate that 25.6% of the neglected children presented clinically significant morphosyntactic difficulties, as evidenced by a significantly shorter MLU ( M = 5.60, SD = 1.13; M = 6.90, SD = 1.30), fewer verbal inflections, and more frequent word omission errors compared to their non-neglected peers. The results confirm that French-speaking neglected children present many morphosyntactic difficulties. This study argues for sustained speech–language services for these children.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".