Acquisition of Mandarin tones by Canadian first graders: Effect of prior exposure to tonal and non-tonal languages
Bibliographic record
Abstract
This study examines the tone productions of school-aged children with and without a tonal language background who are learning Mandarin as a second language (L2) or heritage language in Mandarin-English bilingual schools in Western Canada. Tones are frequently identified as one of the most challenging aspects of phonology for Mandarin L2 learners to acquire. In this study, tone productions of bilingual children from three home language backgrounds, English, Cantonese, and Mandarin Chinese, were compared for transcribed accuracy using mixed effects logistic regression. In addition, the fundamental frequency contours of correct tone productions were fitted with generalized additive mixed models to analyse the acoustic differences between groups. Error patterns were also analysed for possible Cantonese substitutions. Our results suggest that children with a Cantonese background are more accurate in tone productions than children with an English language background, but they also made more errors than their peers with a Mandarin language background. These findings suggest that a tonal language background could result in positive transfer among school-age children who are in the early stages of learning Mandarin as an L2.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".