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Record W3201762137 · doi:10.1037/edu0000672

How you read affects what you gain: Individual differences in the functional organization of the reading system predict intervention gains in children with reading disabilities.

2021· article· en· W3201762137 on OpenAlexaff
Noam Siegelman, Jay G. Rueckl, Mark van den Bunt, Jan C. Frijters, Jason D. Zevin, Maureen W. Lovett, Mark S. Seidenberg, Kenneth R. Pugh, Robin D. Morris

Bibliographic record

VenueJournal of Educational Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of TorontoBrock University
FundersNational Institutes of HealthEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentIsrael Science Foundation
KeywordsPseudowordReading (process)PsychologyIntervention (counseling)Cognitive psychologyReading comprehensionTask (project management)ComprehensionWord recognitionPsychological interventionDevelopmental psychologyReading disabilityCognitionLinguisticsDyslexia

Abstract

fetched live from OpenAlex

graders) rely on two sources of information during an oral word reading task - print-speech correspondences and semantic imageability - before and after a phonologically-weighted intervention. We show that children who relied more on print-speech regularities and less on imageability pre-intervention had better intervention gains. In parallel, children who over the course of the intervention exhibited greater increases in their reliance on print-speech correspondences and greater decreases in their reliance on imageability had better intervention outcomes. Importantly, these two factors were differentially related to specific reading task outcomes, with greater reliance on print-speech correspondences associated with pseudoword naming, while (lesser) reliance on imageability related to word reading and comprehension. We discuss the implications of these findings for theoretical models of reading acquisition and educational practice.

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 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)0.001
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 score0.017
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

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

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 head0.032
GPT teacher head0.312
Teacher spread0.280 · 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 teacher head, 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

Citations18
Published2021
Admission routes1
Has abstractyes

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