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Record W2078458608 · doi:10.1080/10888430903034796

RAN Components and Reading Development From Grade 3 to Grade 5: What Underlies Their Relationship?

2009· article· en· W2078458608 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.

Bibliographic record

VenueScientific Studies of Reading · 2009
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsQueen's UniversityUniversity of Alberta
Fundersnot available
KeywordsRapid automatized namingFluencyReading (process)CognitionCognitive psychologyOrthographyPsychologyComputer sciencePhonologyPhonological awarenessLinguistics

Abstract

fetched live from OpenAlex

We examined (a) how rapid automatized naming (RAN) components—articulation time and pause time—predict reading accuracy and reading fluency in Grades 4 and 5, and (b) what cognitive-processing skills (phonological processing, orthographic processing, or speed of processing) mediate the RAN–reading relationship. Sixty children were followed from Grade 3 to Grade 5 and were administered RAN (Letters and Digits), phonological processing, lexical and sublexical orthographic processing, speed of processing, reading accuracy, and fluency tasks. Pause time was highly correlated with reading fluency and shared more of its predictive variance with lexical orthographic processing and speed of processing than with phonological processing. Articulation time also predicted reading fluency, and its contribution was mostly independent from other cognitive-processing skills. Implications for the relationship between RAN and reading 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.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.123
GPT teacher head0.357
Teacher spread0.235 · 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