No prosody-syntax trade-offs: Prosody marks focus in Mandarin cleft constructions
Bibliographic record
Abstract
In line with the idea that language has evolved to be efficient and to avoid redundancy, syntactic means of marking information structure have been derived from prosodic ones, and vice versa, for many languages. On the basis of crosslinguistic comparisons, prosody-syntax trade-offs have frequently been described for clefts. The present study investigated whether such trade-offs can also be observed language-internally, testing whether clefting reduced prosodic focus marking in production or its effects on perception in Mandarin. A production study found that clefts showed prosodic focus marking equal to or exceeding that found in syntactically unmarked equivalents. In both syntactic conditions, focused constituents had larger f0 ranges, higher f0 maxima and longer durations compared to a broad focus baseline, while post-focal constituents showed lower f0 maxima and minima, lower intensity and, for clefts, shorter durations (28 participants, 937 utterances containing 4466 syllables analyzed in total). A rating study likewise found that the effect of prosody on the perception of information structure was not modulated by clefting, which neither affected ratings nor reaction times (102 participants, 2448 responses analyzed in total). These findings suggest that prosody is integral for marking focus in cleft constructions instead of constituting a redundant cue.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".