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Record W2119587501 · doi:10.1177/00222194070400050801

Innovative Programs for Improvement in Reading Through Cognitive Enhancement

2007· article· en· W2119587501 on OpenAlexaffabout
Denyse V. Hayward, J. P. Das, Troy Janzen

Bibliographic record

VenueJournal of Learning Disabilities · 2007
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRemedial educationPsychologyReading (process)CognitionMultivariate analysis of variancePhonological awarenessDevelopmental psychologyMathematics educationPedagogyLiteracyLinguistics

Abstract

fetched live from OpenAlex

Forty-five Grade 3 students from a reservation school in Western Canada were divided into two remedial groups and a no-risk control group. One remedial group was given a classroom-administered cognitive enhancement program (COGENT) throughout the school year. The second group received COGENT for the first half of the year followed by a pull-out cognitive-based reading enhancement program (PREP). Children were assessed across phonological awareness, rapid naming, reading, and cognitive ability at the beginning of the year, mid-term, and at the end of the school year. MANOVA results showed a significant interaction for reading measures, with students receiving classroom intervention over the school year making the greatest gains. Results are discussed in terms of group, remediation program, and individual participant improvements.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.368
Teacher spread0.330 · 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

Citations50
Published2007
Admission routes2
Has abstractyes

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