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Record W2041853698 · doi:10.1177/0022219408317859

Interventions for Reading Difficulties

2008· article· en· W2041853698 on OpenAlexaff
Maureen W. Lovett, Maria De Palma, Jan C. Frijters, Karen A. Steinbach, Meredith Temple, Nancy Benson, Léa Lacerenza

Bibliographic record

VenueJournal of Learning Disabilities · 2008
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsBrock UniversitySickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsReading (process)PsychologyIntervention (counseling)Psychological interventionResponse to interventionReading disabilitySpecial educationDevelopmental psychologyDyslexiaLinguisticsMathematics education

Abstract

fetched live from OpenAlex

This article explores whether struggling readers from different primary language backgrounds differ in response to phonologically based remediation. Following random assignment to one of three reading interventions or to a special education reading control program, reading and reading-related outcomes of 166 struggling readers were assessed before, during, and following 105 intervention hours. Struggling readers met criteria for reading disability, were below average in oral language and verbal skills, and varied in English as a first language (EFL) versus English-language learner (ELL) status. The research-based interventions proved superior to the special education control on both reading outcomes and rate of growth. No differences were revealed for children of EFL or ELL status in intervention outcomes or growth during intervention. Oral language abilities at entry were highly predictive of final outcomes and of reading growth during intervention, with greater language impairment being associated with greater growth.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.064
GPT teacher head0.360
Teacher spread0.296 · 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

Citations88
Published2008
Admission routes1
Has abstractyes

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