A Longitudinal Investigation of Oral Inferential Comprehension in French-Speaking Preschoolers: Results from the ELLAN Study
Bibliographic record
Abstract
INTRODUCTION: Inferential comprehension is a complex language skill fundamental for social competence and reading comprehension. Evidence from the literature demonstrates that this ability develops early in a child's life. Using a longitudinal design, this study aimed to describe the early developmental trajectory of oral inferential comprehension ability in young typically developing French-speaking children from 3;6 to 5;6 years of age. METHODS: A narrative-based oral inferential comprehension task was administered to a group of typically developing children (n = 79) at 3;6, 4;6, and 5;6 years old; as part of the Early Longitudinal Language and Neglect [ELLAN] study. A total of 19 inferential questions were classified into six types of causal inferences targeting the comprehension of story grammar elements. RESULTS: Inferential comprehension total scores showed significant improvement across all three time points, with the most significant increase between 3;6 and 4;6 years of age. At 3;6 years old, questions about the problem of the story, goal, and characters' internal responses were better answered compared to questions about the attempts to solve the problem, predictions, and the story's resolution. By 5;6 years of age, no ceiling effects were observed for any of the inference types, indicating ongoing development of inferential comprehension ability. CONCLUSION: Such longitudinal data documenting a developmental sequence of early oral inferential comprehension supports the importance of initiating assessments and interventions of this complex skill from an early age, especially from 3;6 to 4;6 years old, a period marked by significant growth.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".