MétaCan
Menu
Back to cohort

Children's inference generation across different media

2008· article· en· W2047493729 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Research in Reading · 2008
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsMcGill University
FundersNational Institutes of HealthNational Institute of Child Health and Human DevelopmentMcGill University
KeywordsVocabularyComprehensionInferenceNarrativePsychologySet (abstract data type)Point (geometry)Vocabulary developmentLanguage developmentLinguisticsCognitive psychologyDevelopmental psychologyComputer scienceArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

In the present study, we investigated the degree to which children's inference generation ability generalises across different media and predicts narrative comprehension over and above basic language skills and vocabulary. To address both aims, we followed two cohorts of children aged 4 and 6 as they turned 6 and 8 years old, respectively. At each time point we assessed their inference and narrative comprehension skills using aural, televised and written stories. We also assessed their basic language skills and vocabulary. The findings demonstrated that children's inference generation skills were highly inter‐related across different media for both cohorts and at both time points. Also, children's inference generation had a significant contribution to children's narrative comprehension over and above basic language skills, vocabulary and media factors. The current set of findings has important theoretical and practical implications for early diagnosis and intervention in young children's high‐order comprehension skills.

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.

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)0.001
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 score0.075
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.190
GPT teacher head0.479
Teacher spread0.289 · 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