Early and late nuclei in yes-no questions: tails or high rises?
Bibliographic record
Abstract
In this paper we analyze the placement of the nucleus in yesno questions and its significance in American English.We show that the vast majority of positive yes-no questions are expressed through a low rise, often with at least one word as a tail.High rise is the second most common yes-no question nucleus; and so we are interested in the question of why a high-rise instead of a low rise contour is sometimes selected by the speaker.It can be difficult to tell whether a question should end in a late high rise, or whether an early low-rise should be postulated with a tail that is part of the rise.We bring phonetic criteria to bear on this question, and also show that post-nuclear tails tend to consist of function words or else of information that is in some sense given in the discourse.Finally, we present evidence that the discourse function of high rises overlaps with the function of tails to such an extent that it is economical to consider the high pitch accent of a high rise nuclear tune as simply an accented part of what otherwise could be analyzed as a tail.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".