Bibliographic record
Abstract
For this paper, we examined a corpus of 73 wh-questions and yes/no questions, both positive and negative, from natural discourse.We found that the locus of interrogation (the initial auxiliary in yes/no questions or the initial wh-word in whquestions) most frequently gets an L+H* pitch accent, especially in wh-questions and negative yes/no questions.Positive yes/no questions are more variable, and included 40% unstressed auxiliaries.Nuclear stress was primarily falling in wh-questions, as expected; but positive yes/no questions were almost twice as often falling or level as rising, contrary to expectation.Finally, the topic of the question turned out to be marked primarily with some version of an H* accent rather than an L+H* accent, and the focus with L+H* rather than some variant of H*, contrary to predictions in the literature. CommentA predication, P, is the comment of a sentence, S, iff, in using S the speaker intends P to be assessed relative to the topic of S. FocusThat part of the linguistic expression that realizes the comment.What is interesting about these definitions is that questions and other speech acts as well as statements can be said to have information (topic-focus) structure that doesn't necessarily coincide with any particular syntactic distinction.It is possible that there could be a divergence between the semantic
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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 | 0.008 |
| 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 | 0.001 |
| 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".