Persistence of Phonological Processing Deficits in College Students with Dyslexia Who Have Age-Appropriate Reading Skills
Bibliographic record
Abstract
This study investigated the phonological processing skills of university students with dyslexia. Fifty-nine students participated in this study: 28 with reading disabilities based on recent psychological assessments and a history of early and persistent reading problems; and 31 controls. The two groups did not differ on estimates of verbal and nonverbal abilities. The dyslexia group performed significantly less well on standardized measures of reading and spelling. However, the dyslexia group scores on these measures fell within the average range. The main dependent variables were subsumed under three areas of phonological processing: phonological awareness, phonological recoding in lexical access, and phonological recoding in working memory. The control group performed significantly better on all phonological processing measures, particularly those measures involving accuracy and response times. Despite age-appropriate performances on standardized reading and spelling measures, phonological processing deficits persisted in the dyslexia group. These findings support the causal role of phonological awareness in the acquisition of reading skills and indicate that differences in phonological processing skills are still evident in a sample of university students with dyslexia compared a group matched on age and education.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".