Reading and spelling profiles of adult poor readers: Phonological, orthographic and morphological considerations
Bibliographic record
Abstract
Reading and spelling skills are important to communicate in today's literate society, however, the underlying processes of spelling skills are under-researched compared to reading skills. Our goals were to (a) study how the component skills of phonological, orthographic and morphological awareness are different in adults with and without reading difficulties, and (b) characterize the relationship between the component skills and reading and spelling performance in both skilled and poor readers. Participants (N = 37, N = 15 with reading impairments and N = 22 skilled readers) took part in the study where they completed several literacy-based measures. We performed a series of mixed ANOVAs to study the between-group differences in performance and the relationship between different literacy outcomes, respectively. We found evidence for poor phonological and morphological awareness in the poor readers compared to the skilled readers. We also found differential relationships between the component skills and reading and spelling behavior. Specifically, sound awareness was significantly related to reading and spelling measures in the skilled readers, whereas morphological and sound awareness played an important role in the same skills in the poor readers. We discuss these findings in the context of potential remediation strategies for adults with persistent literacy impairments.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".