Remediating the Core Deficits of Developmental Reading Disability
Bibliographic record
Abstract
The double-deficit hypothesis (Wolf, 1997; Wolf & Bowers, 1999, this issue) contends that deficits in phonological awareness and deficits in visual naming speed represent two independent causal impediments to reading acquisition for children with developmental reading disabilities (RD). One hundred and sixty-six children with severe RD from 7 to 13 years of age were classified into three deficit subgroups according to a double-deficit framework. A total of 140 children with RD, 84% of the sample, were classified; 54% demonstrated a double deficit (DD), 22% a phonological deficit only (PHON), and 24% a visual-naming speed deficit only (VNS). Diagnostic test profiles highlighted the joint contributions of the two core deficits in depressing written language acquisition. The children in the DD group were more globally impaired than those in the other subgroups, and the VNS group children were the highest achieving and most selectively impaired readers. Following 35 hours of word identification training, sizable gains and significant generalization of training effects were achieved by all subgroups. A metacognitive phonics program resulted in greater generalized effects across the domain of real English words, and a phonological training program produced superior outcomes within the phonological processing domain. The greatest non-word reading gains were achieved by children with only phonological deficits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".