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Record W2900914029 · doi:10.1017/s0142716418000681

Orthographic processing and children’s word reading

2018· article· en· W2900914029 on OpenAlexafffund
S. Hélène Deacon, Adrian Pasquarella, Eva Marinus, Talisa Tims, Anne Castles

Bibliographic record

VenueApplied Psycholinguistics · 2018
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOrthographic projectionReading (process)PsychologyLinguisticsWord recognitionOrthographyCognitive psychologyStructural equation modelingWord (group theory)Learning to readNatural language processingComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

ABSTRACT Theories of reading development generally agree that, in addition to phonological decoding, some kind of orthographic processing skill underlies the ability to learn to read words. However, there is a lack of clarity as to which aspect(s) of orthographic processing are key in reading development. We test here whether this is orthographic knowledge and/or orthographic learning. Whereas orthographic knowledge has been argued to reflect a child’s existing store of orthographic representations, orthographic learning is concerned with the ability to form these representations. In a longitudinal study of second- and third-grade students, we evaluate the relations between these two aspects of orthographic processing and word-reading outcomes. The results of our analyses show that variance captured by orthographic knowledge overlaps with that of word reading, to the point that they form a single latent word-reading factor. In contrast, orthographic learning is distinctive from this factor. Further, structural equation modeling demonstrates that early orthographic learning was related to gains in word reading skills. We discuss the implications of these findings for theories of word-reading development.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.016
GPT teacher head0.311
Teacher spread0.295 · 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

Citations45
Published2018
Admission routes2
Has abstractyes

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