Perception or production? Training effects on cross-language phonological awareness tasks in Mandarin-speaking children.
Bibliographic record
Abstract
This study investigated training effects of perception-based and production-based English activities on the acquisition of phonological awareness of English sound structures by 58 Mandarin-speaking kindergarten-aged children in Taiwan. Children were randomly assigned to one of two experimental conditions or a control condition. Experimental groups participated in a learning session of four English words: band, nest, brick, and stool, with a perceptual or articulation focus on clusters, which do not exist in Mandarin. Outcome measures examined subjects’ ability to match words on the basis of shared onset or coda, and to elicit common units in both Mandarin and English before and after training. Significant gain in English onset common unit test scores was observed for the production training group, relative to the control group, especially in trials containing the trained clusters: br- and st-. Perception group however did not show an improvement over time across all measures. Between-group differences were not observed for English codas, Chinese onsets, or Chinese codas. Findings revealed that clearly articulating non-native words with unfamiliar syllable structures could assist children to isolate onsets in explicit phonological awareness tasks, independent of exposure to alphabetic orthography, generally thought to be critical in explicit phonological awareness (e.g., Gombert, 1992).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".