Examining the contribution of RAN components to reading fluency, reading comprehension, and spelling in German
Bibliographic record
Abstract
Abstract We examined the contribution of rapid automatized naming (RAN) components (articulation time, pause time, and pause time consistency) to reading fluency, reading comprehension, and spelling in a sample of 257 German children (139 boys, 118 girls; M age = 5.60 years, SD = 0.31) followed from kindergarten to Grade 1. In kindergarten, children were assessed on measures of RAN (colors and objects), phonological awareness, letter-sound knowledge, phonological short-term memory, and paired-associate learning. Reading fluency, reading comprehension, and spelling were assessed at the end of Grade 1. Hierarchical regression analyses revealed that pause time and pause time consistency continued to predict reading fluency, but not reading comprehension or spelling, after controlling for the effects of the other cognitive skills assessed in kindergarten. Articulation time did not add to the prediction of any literacy skills. These findings support previous research suggesting that, during the early phases of learning to read, pause time holds the key in the relation between RAN and reading fluency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".