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Simple but complex: components of the simple view of reading across grade levels

2009· article· en· W2056385340 on OpenAlexaff
Janet Tilstra, Kristen L. McMaster, Paul van den Broek, Panayiota Kendeou, David N. Rapp

Bibliographic record

VenueJournal of Research in Reading · 2009
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsMcGill University
FundersInstitute of Education Sciences
KeywordsReading comprehensionFluencyPsychologyReading (process)Variance (accounting)ComprehensionListening comprehensionActive listeningCognitive psychologyDevelopmental psychologyMathematics educationLinguisticsCommunication

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the simple view of reading (SVR) and contributions of verbal proficiency and reading fluency to reading comprehension for fourth‐, seventh‐ and ninth‐grade readers (N=271). The SVR explained a significant proportion of variance in reading comprehension for all grades with decreasing explained variance in higher grades. The variance explained by decoding decreased from fourth grade to higher grades. The variance explained by listening comprehension increased from fourth‐ to seventh‐grade, but did not change from seventh‐ to ninth‐grade. In all grades, verbal proficiency and reading fluency contributed substantial additional variance to reading comprehension beyond the SVR. Changes in the predictive relation between listening and reading comprehension and factors influencing reading comprehension in each grade 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)0.006
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.260
GPT teacher head0.501
Teacher spread0.241 · 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

Citations335
Published2009
Admission routes1
Has abstractyes

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