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Sensitivity to linguistic stress, phonological awareness and early reading ability in preschoolers

2010· article· en· W2052912771 on OpenAlexaff
Ilana Goodman, Amanda Libenson, Lesly Wade‐Woolley

Bibliographic record

VenueJournal of Research in Reading · 2010
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsQueen's University
Fundersnot available
KeywordsPhonological awarenessPsychologyReading (process)Stress (linguistics)PhonologyLinguisticsPhonemic awarenessLanguage developmentPhonological developmentCognitive psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Recent research has found that sensitivity to linguistic stress is related to phonological awareness and reading development. This study investigated the roles of two types of linguistic stress sensitivity (lexical and metrical stress) in the phonological awareness and reading development of young children. Forty-five kindergarten children were tested on a battery of tasks that examined linguistic stress sensitivity and early reading ability. Results indicated that lexical stress, but not metrical stress sensitivity, is significantly related to phonological awareness and early reading ability. However, lexical stress is not able to predict unique variance in early reading ability once phonological awareness is controlled for. The relationships of both lexical and metrical stress sensitivity with phonological awareness and early reading development are discussed.

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)0.003
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.071
GPT teacher head0.433
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 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

Citations45
Published2010
Admission routes1
Has abstractyes

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