Comprehension of Inferences in a Narrative in 3- to 6-Year-Old Children
Bibliographic record
Abstract
Purpose: This study aimed to describe the development of inferential abilities of children age 3 to 6 years in a narrative using a dialogic reading task on an iPad. Method: Participants were 121 typically developing children, divided into 3 groups according to age range (3-4 years old, 4-5 years old, 5-6 years old). Total score of inferential comprehension, subscores by causal inference type targeting elements of the story grammar, and quality of response were examined across groups. Results: Inferential comprehension emerged early, from 3 to 4 years old, with considerable interindividual variability. Inferential comprehension scores increased significantly in relation to age, leading to developmental steps with regards to the type of causal inferences. The ability to infer the problem of the story, the internal response of a character, and predictions were easier starting at age 4 years. Then, the 5- to 6-year-olds were better able to infer the goal, the attempt to solve the problem, and the resolution. Last, between the ages of 3 and 6 years, children improved in terms of the quality of response they provided. Conclusion: This study addresses important gaps in our knowledge of inferential comprehension in young children and has implications for planning of early education in this realm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".