Cantonese Tone Perception by Punjabi Speakers of Cantonese: Evidence and Implications for the Perceptual Assimilation Model of Second Language Speech Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it