Lexical tone processing by monolingual and bilingual speakers of tone and non tone languages
Bibliographic record
Abstract
This experiment tested early Chinese–English bilinguals (age of arrival in Canada < 2 years) for lexical encoding processes of F0 contour (tone). Earlier experimentation (e.g., Mattock and Burnham, 2006) has indicated that processing of lexical tone differs between the two corresponding monolingual groups, demonstrating that English monolingual infants begin to disregard F0 contour differences in non word minimal pairs by the age of nine months, while Chinese monolingual infants do not exhibit this behavior. In the present study, three groups of participants were tested using a short-term memory encoding task (Dupoux et al., 2010) with non word minimal pairs differentiated only by F0 contour. Although the tone contours utilized in the experiment were non-native to all speakers, Chinese-dominant bilinguals (adult arrival in Canada) performed significantly better than English monolinguals in recalling long non word sequences differentiated only by these contours, while their performance in simple phoneme-differentiated sequences (e.g., [mu, fu]) was equal to that of the English speakers. However, the target group of early Chinese–English bilinguals produced scores corresponding to a bimodal distribution, with some speakers correlating to English monolinguals' performance and others corresponding to Chinese-dominant speakers' scores. Correspondence to one mode or another was analyzed using a series of sociolinguistic factors.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".