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Correlates of orthographic learning in third‐grade children's silent reading

2007· article· en· W2148150170 on OpenAlexfundno aff
Judith A. Bowey, Robyn L. Miller

Bibliographic record

VenueJournal of Research in Reading · 2007
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
FundersAustralian Research CouncilLeading Edge Endowment Fund
KeywordsHomophonePsychologyOrthographic projectionReading (process)SpellingOrthographyPhonologyCognitive psychologyLinguisticsWord recognitionArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

This study examined word identification, phonological recoding efficiency, familiar word reading efficiency, orthographic choice for familiar words and serial naming speed as potential correlates of orthographic learning following silent reading in third‐grade children. Children silently read a series of short stories, each containing six repetitions of a different target non‐word. They subsequently read target non‐words faster than homophones and preferred target non‐words to homophones in an orthographic choice task, indicating that they had formed functional orthographic representations of the target non‐words through phonologically recoding them during silent story reading. Target non‐word orthographic choice was correlated with all measures bar non‐symbol naming speed. The association between phonological recoding efficiency and orthographic learning lends support to the hypothesis that self‐teaching occurs through phonological recoding even in silent reading. Our findings were not generally consistent with the view that serial naming speed assesses orthographic learning aptitude.

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)0.000
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.051
GPT teacher head0.414
Teacher spread0.363 · 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

Citations67
Published2007
Admission routes1
Has abstractyes

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