Inferential Comprehension Abilities in French-Speaking Preschoolers Exposed to Neglect in the Early Longitudinal Language and Neglect Study
Bibliographic record
Abstract
Purpose: Using a longitudinal design, this study aimed to describe inferential comprehension abilities of neglected French-speaking preschool children from 42 to 66 months of age in comparison to non-neglected peers, to examine the association with receptive vocabulary, and to determine whether rates of change in inferential abilities over time was stable between the two group conditions. Method: An inferential comprehension task and the French version of the Peabody Picture Vocabulary Test–Fourth Edition were administered to a group of neglected children ( n = 37–40) and to a group of same-age non-neglected children ( n = 71–91) at 42, 54, and 66 months old, as part of the Early Longitudinal Language and Neglect study. Results: Results show that children exposed to neglect obtain significantly lower scores compared to their same-age peers on inferential comprehension and receptive vocabulary measures at all three time points ( p < .001) with large to very large effect sizes and indicate moderate to strong correlations between the two variables. Children from the neglected group present difficulties in inferencing compared to same-age non-neglected peers, a disadvantage that remains stable over time. Conclusions: This study demonstrates the significant gap in inferential comprehension abilities between neglected and non-neglected preschool children. These results reiterate the importance of early detection of language comprehension difficulties in young children coming from vulnerable environments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".