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Record W2094900272 · doi:10.1177/0142723714525945

Relationships between preschoolers’ oral language and phonological awareness

2014· article· en· W2094900272 on OpenAlexaff
Kathleen Hipfner-Boucher, Trelani Milburn, Elaine Weitzman, Janice Greenberg, Janette Pelletier, Luigi Girolametto

Bibliographic record

VenueFirst Language · 2014
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhonological awarenessNarrativePsychologyVocabularyReading (process)LinguisticsDevelopmental psychologyMultilevel modelCognitive psychologyNonverbal communicationComputer science

Abstract

fetched live from OpenAlex

This study examines the relationship between complex oral language and phonological awareness in the preschool years. Specifically, the authors investigate the relationship between concurrent measures of oral narrative structure (based on measures of both story retell and generation), and measures of blending and elision in a sample of 89 children between 4 and 6 years of age. A hierarchical linear regression was conducted to determine whether oral narrative structure explained unique variance in skill in blending and elision over and above that explained by vocabulary and after controlling for a number of factors known to contribute to phonological awareness outcomes (age, nonverbal reasoning ability, phonological memory, letter knowledge, word reading). The results of the study support the authors’ hypothesis of an association between narrative structure and phonological awareness, and between vocabulary and phonological awareness. The findings are interpreted within a theoretical framework that posits that common structural and processing demands underlie oral narrative discourse and phonological awareness.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.315
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), 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

Citations61
Published2014
Admission routes1
Has abstractyes

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