Development of Extrinsic Normalization of Lexical Tones in Cantonese-Speaking Children
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate the age by which Cantonese-speaking children reach adult level in using contextual cues to adjust for speech variability in identifying level tones. Another aim of this study was to explore the external and internal factors on the level tone normalization, that is, the influence of context type and individual attributes including linguistic skill and musical pitch sensitivity. METHOD: The study involved 62 Cantonese-speaking children aged 7-10 years (31 boys, 31 girls) and 24 young adults (12 men, 12 women). Participants were asked to identify Cantonese level tones in different conditions: condition without context and condition with contexts: speech, music, or pure tone. Child participants' linguistic skill was assessed using a subtest of the standardized language test, and their sensitivity to musical pitch changes was assessed using three subtests related to pitch perception of Montreal Battery of Evaluation of Musical Abilities. RESULTS: Children aged 8 years and above showed comparable performance with adults in the condition with speech context, and performed significantly better than younger children. Nonspeech contexts (music and pure tone) did not elicit contrastive context effect in participants across all age groups. The children with better linguistic skill or higher musical pitch sensitivity performed better in using speech contextual cues to identify level tones. CONCLUSIONS: Cantonese-speaking children matured in their ability to normalize level tones at age of 8 years. This ability was positively associated with linguistic skill and musical pitch sensitivity. In addition, Cantonese level tone normalization is a speech-specific perceptual process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 0.001 |
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".