Decomposing the relation between Rapid Automatized Naming (RAN) and reading ability.
Why this work is in the frame
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Bibliographic record
Abstract
The Rapid Automatized Naming (RAN) test involves rapidly naming sequences of items presented in a visual array. RAN has generated considerable interest because RAN performance predicts reading achievement. This study sought to determine what elements of RAN are responsible for the shared variance between RAN and reading performance using a series of cognitive tasks and a latent variable modelling approach. Participants performed RAN measures, a test of reading speed and comprehension, and six tasks, which tapped various hypothesised components of the RAN. RAN shared 10% of the variance with reading comprehension and 17% with reading rate. Together, the decomposition tasks explained 52% and 39% of the variance shared between RAN and reading comprehension and between RAN and reading rate, respectively. Significant predictors suggested that working memory encoding underlies part of the relationship between RAN and reading ability.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it