A Taxometric Investigation of Developmental Dyslexia Subtypes
Bibliographic record
Abstract
Long‐standing issues with the conceptualization, identification and subtyping of developmental dyslexia persist. This study takes an alternative approach to examine the heterogeneity of developmental dyslexia using taxometric classification techniques. These methods were used with a large sample of 671 children ages 6–8 who were diagnosed with severe reading disorders. Latent characteristics of the sample are assessed in regard to posited subtypes with phonological deficits and naming speed deficits, thus extending prior work by addressing whether these deficits embody separate classes of individuals. Findings support separate taxa of dyslexia with and without phonological deficits. Different latent structure for naming speed deficits was found depending on the definitional criterion used to define dyslexia. Non‐phonologically based forms of dyslexia showed particular difficulty with naming speed and reading fluency. Copyright © 2012 John Wiley & Sons, Ltd. Practitioner Points Support for separate subtypes of dyslexia, with and without phonological deficits (PDs), indicates a need for different approaches to intervention. A discrepancy‐based criterion identifies more non‐PD cases that may be missed with a response‐to‐intervention diagnosis. Sound symbol correspondence and decoding measures may best distinguish cases of dyslexia with and without PDs.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".