Cue weighting in the tonal register contrast of Jiashan Wu
Bibliographic record
Abstract
Chinese Wu dialects are known to have two tonal registers, where the lower register is realized with lower pitch and breathy phonation and the upper register is realized with higher pitch and modal phonation. In Jiashan Wu, the falling tone is realized differently in the two registers: the pitch contour of the upper register is slightly steeper than the lower register. This study investigates how speakers of Jiashan Wu weight the three cues (i.e., breathiness, pitch height, pitch contour) in the register contrast. We recorded two words /ka/ from the upper and lower register and created stimuli varying in both dimensions (5 steps pitch height x 5 step breathiness = 25 stimuli) and imposed the two contours on all stimuli. 28 native listeners performed a forced-choice categorization task on 5 repetitions of each stimuli in random order. A mixed effect logistic model shows that all three factors affect categorization, and that pitch contour is the most important cue and breathiness the least. Moreover, the effect of breathiness was smaller with higher pitches and a steep contour, and the effect of pitch height is smaller with a steep contour. Data are being collected comparing Jiashan and Shanghai dialects.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".