Cantonese Tone Perception by Punjabi Speakers of Cantonese: Evidence and Implications for the Perceptual Assimilation Model of Second Language Speech Learning
Bibliographic record
Abstract
This study tested whether the Perceptual Assimilation Model of Second Language Speech Learning (PAM-L2) predicts second language (L2) Cantonese tone discrimination across different perceptual modes. Punjabi speakers of Cantonese completed the Cantonese tone assimilation and discrimination tasks. In the assimilation task, the Punjabi listeners assimilated the Cantonese tones as two-category (TC), single-category (SC), uncategorized-categorized without overlap (UC-no), and uncategorized-categorized with partial overlap (UC-po) pairs, yielding testable predictions for PAM-L2 in the discrimination task (TC = UC-no > UC-po > SC). In the discrimination task, the model-driven predictions were largely supported in the double-talker context but not in the single-talker and pure tone contexts. These results suggest that PAM-L2 applies to phonological but not non-phonological discrimination of L2 tones. Moreover, our findings indicate that the distinction between partial and complete overlap may not be necessary for UC pairs.
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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 | 0.002 |
| 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 | 0.001 |
| 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".