Bibliographic record
Abstract
In a corpus study using eight pairs of syntactically ambiguous sentences, we conducted acoustic and quantitative analyses of the prosodic patterns used by seven native speakers of the Hong Kong variety of Cantonese to divide these sentences into prosodic words -a phenomenon referred to as "prosodic chunking".Although pauses, pitch reset and preboundary lengthening were analyzed, in this article we concentrate on presenting results from the analysis of preboundary lengthening.Using test sentence pairs consisting of identical series of words with two possible prosodic subdivisions, we measure the preboundary syllables within the sentence, and compare it to the same syllables in the non-boundary position of the corresponding sentence.Results indicate that the presence/absence of a following prosodic boundary is highly significant in the measure of lengthening, thus confirming preboundary lengthening as a prominent device in marking prosodic word boundaries in Cantonese.Moreover, the presence of a pause at the prosodic word boundary also triggers a more prominent preboundary lengthening.Finally, our statistical results indicate that there seems to be a trade-off relation between pitch reset and preboundary lengthening.Since this result seems to contradict with recent research, which indicates that pitch range increases with syllabic duration [1], and that pitch contours are also subject to contextual tonal effects (both carry-over and anticipatory) [2] as well as perturbations brought about by focus [3].As a result, more research is needed before we can confirm/disconfirm the validity of this trade-off relation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".