Inferential comprehension of 3-6 year olds within the context of story grammar: a scoping review
Bibliographic record
Abstract
BACKGROUND: The ability to make inferences plays a crucial role in reading comprehension and the educational success of school-aged children. However, it starts to unfold much earlier than school entry and literacy. Given that it is likely to be targeted in speech language therapy, it would be useful for clinicians to have access to information about a developmental sequence of inferential comprehension. Yet, at this time, there is no clear proposition of the way in which this ability develops in young children prior to school entry. AIMS: To reduce the knowledge gap with regards to inferential comprehension in young children by conducting a scoping review of the literature. The two objectives of this research are: (1) to describe typically developing children's comprehension of causal inferences targeting elements of story grammar, with the goal of proposing milestones in the development of this ability; and (2) to highlight key elements of the methodology used to gather this information in a paediatric population. METHODS & PROCEDURES: A total of 16 studies from six databases that met the inclusion criteria were qualitatively analysed in the context of a scoping review. This methodological approach was used to identify common themes and gaps in the knowledge base to achieve the intended objectives. MAIN CONTRIBUTION: Results permit the description of key elements in the development of six types of causal inference targeting elements of story grammar in children between 3 and 6 years old. Results also demonstrate the various methods used to assess this ability in young children and highlight particularly interesting procedures for use with this younger population. CONCLUSIONS: These findings point to the need for additional studies to understand this ability better and to develop strategies to stimulate an evidence-based developmental sequence in children from an early age.
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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 | 0.015 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".