Prevalence and Reliability of Phonological, Surface, and Mixed Profiles in Dyslexia: A Review of Studies Conducted in Languages Varying in Orthographic Depth
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.
Bibliographic record
Abstract
The influence of orthographic transparency on the prevalence of dyslexia subtypes was examined in a review of multiple-case studies conducted in languages differing in orthographic depth (English, French, and Spanish). Cross-language differences are found in the proportion of dissociated profiles as a function of the dependent variables (speed or accuracy), the classification method (classical vs. regression-based methods), and the control sample (chronological age vs. reading level controls). The classical method results in a majority of mixed profiles, whereas the regression-based method results in a majority of dissociated profiles. However, the regression-based method appears to result in less reliable subtypes within and between languages. Finally, reading-level comparisons revealed that the phonological subtype reflects a deviant developmental trajectory across all languages, whereas the surface subtype corresponds to a delayed developmental trajectory. The results also indicate that reading speed should be considered to correctly classify dyslexics into subtypes, at least in transparent orthographies.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it