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Record W2010143454 · doi:10.1207/s15548430jlr3401_4

Teaching Decoding Skills to Poor Readers in High School

2002· article· en· W2010143454 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Literacy Research · 2002
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPsychologyReading comprehensionClass (philosophy)Mathematics educationTest (biology)Reading (process)Control (management)ComprehensionAnalysis of covarianceWord (group theory)Computer scienceLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

Thirty-three poor readers participated in an experimental test of a method to teach decoding skills. Twenty-one students in the experimental condition were given approximately 18 sessions of individual tutoring in which they practiced associations between letter patterns and pronunciations for pronounceable parts of words. A control group of 12 students remained in their class instead of receiving the tutoring program. Standardized tests of word identification, word attack, and passage comprehension were administered before and after the tutoring program. Analyses of covariance were used to compare performance of experimental and control participants on the reading measures. Students in the experimental group showed greater improvements on all measures than did students in the control condition when the effects of initial scores were statistically controlled.

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.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.075
GPT teacher head0.444
Teacher spread0.369 · 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