Phonetic and lexical encoding of tone in Cantonese heritage speakers
Bibliographic record
Abstract
Heritage speakers contend with at least two languages: the less dominant L1 (heritage language), and the more dominant L2. Maintaining the heritage language allows heritage speakers to communicate with members of their community. In some cases, their L1 and L2 bear striking phonological differences. In the current study, we investigate this in the context of Toronto-born Cantonese heritage speakers and their maintenance of Cantonese lexical tone, a linguistic feature that is absent from English, the more dominant L2. Across two experiments, Cantonese heritage speakers were tested on their phonetic/phonological and lexical encoding of tone in Cantonese. Experiment 1 was an AX discrimination task with varying inter-stimulus intervals (ISIs), which revealed that heritage speakers discriminated tone pairs with distinct pitch contours better than those with shared contours. Experiment 2 was a medium-term repetition priming experiment, designed to extend the findings of Experiment 1 by examining tone representations at the lexical level. We observed a positive correlation between tone minimal pair priming and English dominance. Thus, while increased English dominance does not affect heritage speakers' phonological-level representations, tasks that require lexical access suggest that heritage Cantonese speakers may not robustly and fully distinctively encode Cantonese tone in lexical memory.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".