Prosodic Effects of Focus and Constituency in Mandarin and in English
Bibliographic record
Abstract
The prosody of an utterance encodes multiple types of information simultaneously, including information status of constituents—for example, by modulations in prosodic prominence to encode focus—and information about syntactic constituent structure—by modulations of prosodic phrasing. According to many prosodic theories, however, focus and constituent structure interact with each in their effects on prominence and phrasing respectively. Focus early in an utterance is sometimes assumed to preempt the realization of tonal events later in the utterance, thus neutralizing syntactically-motivated phrasing distinctions. Other accounts assume that focus and constituent structure exert their effects on prominence and phrasing in an additive way. The current study compares English and Mandarin and investigates to what extent the correlates of focus and constituency interact with each other in shaping the prosody in production. The results show that syntax-induced phrasing distinctions are still encoded post-focally in both languages, providing new evidence for the view that different functions can be encoded orthogonally in prosody. Additionally, we found that while the two languages realize phrasing in roughly same way, they differ in their acoustic realization of focus. Mandarin relies more on F0 modulation than English, and Mandarin lexical tones interact with focus realization.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".