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Record W2093677045 · doi:10.1177/0022219408315814

Rapid Serial Naming Is a Unique Predictor of Spelling in Children

2008· article· en· W2093677045 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

VenueJournal of Learning Disabilities · 2008
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsConcordia UniversityMcGill University
Fundersnot available
KeywordsSpellingPsychologyNonsenseReading (process)OrthographyAlphanumericCognitive psychologyPsycholinguisticsLinguisticsCognitionComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Some previous research has shown strong associations between spelling ability and rapid automatic naming (RAN) after controls for phonological processing and nonsense-word reading ability, consistent with the double-deficit hypothesis in reading and spelling. Previous studies did not, however, control for nonsense-word spelling ability before assessing RAN--spelling associations. In this study, 65 children with poor spelling skills but average reasoning ability completed RAN tasks and spelling, reading, and reasoning tasks. Hierarchical regression analyses revealed that, after controls for chronological age, reasoning ability, and spelling of nonsense words, alphanumeric RAN, but not nonalphanumeric RAN, was still a strong predictor of spelling acquisition. Findings are discussed in terms of single- and double-deficit models of spelling and implications for effective teaching.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.277
Teacher spread0.257 · 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