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Record W2530200818 · doi:10.1044/2016_jslhr-l-15-0252

Comprehension of Inferences in a Narrative in 3- to 6-Year-Old Children

2016· article· en· W2530200818 on OpenAlexafffund
Paméla Filiatrault-Veilleux, Caroline Bouchard, Natacha Trudeau, Chantal Desmarais

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2016
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversité LavalUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsComprehensionPsychologyNarrativeReading comprehensionDevelopmental psychologyRealmGrammarCausal inferenceCognitive psychologyReading (process)LinguisticsMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.424
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2016
Admission routes2
Has abstractyes

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