Comparing the phonological, musical, and general cognitive profiles of early-emerging poor, average, and good readers of Chinese
Bibliographic record
Abstract
Introduction: This study compared the phonological, musical, and general cognitive profiles of early-emerging poor, average, and good readers. Methods: We assessed Cantonese preschool children on Chinese word reading, phonological awareness, lexical tone awareness, musical rhythm perception, musical pitch perception, working memory, and non-verbal intelligence. Results: Early-emerging poor readers exhibited poorer phonological awareness than early-emerging average and good readers, whereas the latter two groups did not differ significantly. In the working memory task, early-emerging good readers outperformed both early-emerging average and poor readers, who performed similarly. No significant group differences were found in lexical tone awareness, musical rhythm perception, musical pitch perception, or non-verbal intelligence. Discussion: The results reflect phonological deficits in early-emerging poor readers. Furthermore, phonological awareness and working memory were useful for identifying early-emerging poor and good readers, respectively. Clinically, these findings imply that early-emerging poor readers may benefit most from initial phonological awareness training, followed by working memory training. Moreover, working memory training may also be beneficial for early-emerging average readers seeking to improve their Chinese word reading.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".