A perceptual perspective on Cantonese tonal mergers-in-progress using lexical categorization
Bibliographic record
Abstract
Cantonese is generally described as having a six tone system, composed of 3 level, 2 rising, and 1 falling tones. Researchers have observed that several of these tones have been merging; for example, Tone (T) 2 and T5 [Bauer et al., LVC 15(2), 211–225 (2003)], T3 and T6, and T4 and T6 [Mok et al., LVC 25(3), 341–370 (2013)]. In perception, discrimination-based paradigms show that Cantonese speakers are slower and poorer at discriminating said merging tone pairs [Mok et al., LVC 25(3), 341–370 (2013); Soo and Monahan, BLS 43(2), 47–54 (2017)]. In this study, we examine these mergers in terms of word identification to develop an understanding of the nature of the mergers. Homeland and heritage Cantonese listeners categorize minimal pairs on an 11-step continuum for T2–T5, T3–T6, and T4–T6. Pictures are used as the lexical endpoints, as many participants in the sample do not read characters. Analysis of the categorization functions provide further empirical data on whether listeners are perceptually merged in a way that affects lexical identification. Additionally, these data will shed light on the taxonomy of the mergers to determine whether these are mergers by expansion, approximation, or transfer.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".