Cognitive and environmental correlates of rapid automatized naming in Chinese kindergarten children.
Bibliographic record
Abstract
Although rapid automatized naming (RAN) is one of the best predictors of reading across languages, its nature remains elusive. In the present study, we aim to elucidate the nature of RAN by examining the cognitive and environmental correlates of RAN. One hundred forty-one second-year kindergarten Chinese children (71 girls, 70 boys; mean age = 58.99 months) were assessed on measures of nonverbal cognitive ability, attention, visual processing, conceptual processing, semantic processing, phonological processing, short-term memory, articulation, speed of processing, RAN (digits and objects), and discrete naming. We also collected information on mothers’ education and occupation, and children’s home learning experiences. The results showed that formal home learning experiences, visual processing, phonological processing, and articulation were unique correlates of both RAN tasks. Semantic processing also correlated significantly with RAN objects. However, controlling for the effects of discrete naming eliminated the effects of most subprocesses on RAN. These findings suggest that RAN is indeed multicomponential, but not all components contribute the same way to RAN performance. Theoretical and practical implications of these findings are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".