The predictive relations between non‐alphanumeric rapid naming and growth in regular and irregular word decoding in at‐risk readers
Bibliographic record
Abstract
We investigated influences of non‐alphanumeric rapid naming on decoding skill growth for regularly and irregularly spelled English words. In a longitudinal study, 52 at‐risk and 69 not‐at‐risk readers were tracked from Grade 1 to Grade 3. Non‐alphanumeric rapid naming ability measured in Grade 1 accounted for unique variance in irregular word decoding in early Grade 2 – strong rapid naming was associated with strong irregular word decoding. An interaction between reading risk status and Grade 1 rapid naming indicated that the influence of Grade 1 rapid naming ability on growth in irregular word decoding was different for at‐risk than not‐at‐risk readers. Non‐alphanumeric rapid naming can have predictive validity as a marker for identifying specific difficulties in learning to read irregular words in at‐risk readers. Results indicate that rapid naming plays a general role in irregular word reading and a specific role in at‐risk readers' growth in irregular word decoding.
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".