The development of phonological awareness and Pinyin knowledge in Mandarin-speaking school-aged children
Bibliographic record
Abstract
PURPOSE: , a Romanised alphabetic system, to facilitate literacy development. This research investigates how Mandarin phonological awareness (PA) develops, and how it interacts with Pinyin in school-aged Mandarin-speaking children in China. METHOD: In Beijing, 182 students in grades two through four (ages ranged between 91 and 135 months) were tested for PA (syllable manipulation and onset-rime oddity tasks) and Pinyin knowledge (Pinyin symbol naming and syllable reading tasks). ANOVAs were used to examine their developmental trajectories. Partial correlations and linear regressions were used to examine the relationships between PA and Pinyin knowledge. RESULT: Syllable awareness has already reached the ceiling level by grade two, while onset-rime awareness is still developing across grades. The ability to name Pinyin symbols decreases over time, while the ability to read syllables written in Pinyin stays invariant across grades. PA and Pinyin knowledge are significantly correlated, and the results of linear regression indicated that the relationship between PA and Pinyin syllable reading is bi-directional. CONCLUSION: This study suggests that Mandarin PA development shows features characteristic of a non-alphabetic language with Pinyin knowledge playing a crucial role. Implications for theory and practice of Mandarin-speaking children's literacy development 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 | 0.000 | 0.001 |
| 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".