Individual and dialect differences in perceiving multiple cues: A tonal register contrast in two Chinese Wu dialects
Bibliographic record
Abstract
This study investigates how multiple cues contribute to multi-dimensional phonological contrasts at both the group level and the individual level, and how dialectal experience shapes listeners’ perceptual strategies. We examine a tonal register contrast in two Chinese Wu dialects signaled by three cues: pitch height, voice quality, and pitch contour. We found that 1) at the group level, cue weights are context-specific, i.e., vary by tone, and some contrasts rely more heavily on multiple cues than others; 2) dialectal experience affects listeners’ perceptual strategy: Shanghai listeners, with their own dialect having a smaller voice quality distinction, do not rely more on the cue even when listening to stimuli with a clear breathy-modal distinction, comparing to Jiashan listeners; 3) individuals’ cue weights are correlated in a positive manner, meaning that some listeners show overall larger cue weights than others; larger variability is found when the contrast has more than one salient cue, in which case individuals have different options of choosing one cue over another as the primary cue and this can work against the positive correlation.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".