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Record W7006730375

The Varieties of Pathways to Dysfluent Reading Comparing Subtypes of Children With Dyslexia at Letter, Word, and Connected Text Levels of Reading

2008· article· en· W7006730375 on OpenAlexfundno aff

Bibliographic record

VenueScholarWorks - Georgia State University (Georgia State University) · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Natural History
Canadian institutionsnot available
FundersNational Institute of Child Health and Human DevelopmentHospital for Sick ChildrenGeorgia State University
KeywordsDyslexiaFluencyReading (process)Word recognitionReading disabilityPerception
DOInot available

Abstract

fetched live from OpenAlex

The majority of work on the double-deficit hypothesis (DDH) of dyslexia has been done at the letter and word levels of reading. Key research questions addressed in this study are (a) do readers with different subtypes of dyslexia display differences in fluency at particular reading levels (e.g., letter, word, and connected text)? and (b) do children with dyslexia identified by either low-achievement or ability–achievement discrepancy criteria show similar differences when classified by the DDH? To address these questions, the authors assessed a sample of 158 children with severe reading impairments in second and third grades on an extensive battery and classified them into three reader subtypes using the DDH. The results demonstrated that the three DDH subtypes exhibited differences in fluency at different levels of reading (letter, word, and connected text), underscoring the separate reading profiles of these subtypes and the different possible routes to dysfluency in reading disabilities. Furthermore, the results suggest that the different patterns among DDH subtypes are primarily driven by the ability–achievement discrepancy group. The implications of these findings are discussed for intervention, reading theory, and a more refined understanding of heterogeneity.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.021
GPT teacher head0.172
Teacher spread0.152 · 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.

Study designQualitative
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

Citations0
Published2008
Admission routes1
Has abstractyes

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