Form-Meaning Relations in Russian Confirmative and Surprise Declarative Questions
Bibliographic record
Abstract
Declarative questions (DQs) are declarative sentences used as questions. As declaratives, they differ from information-seeking polar questions (ISQs) in their syntax, and as biased questions, they differ from polar questions because they can convey various epistemic stances: a request for confirmation, surprise, or incredulity. Most studies on their intonation typically compare just one subtype to ISQs. In this paper, we present a production study where participants pronounced ISQs, confirmative and surprise DQs, and assertions in Russian. We analyzed the pitch and duration of the target utterances, as these prosodic cues proved to be important in the formal markedness of various biased question types across languages. A principal component analysis (PCA) on the pitch contours shows that DQs bear the same rise-fall contour as ISQs, but its peak falls on the stressed syllable of the last word of the sentence instead of the verb. The intonation of surprise DQs differs from that of confirmative ones in that they also exhibit a slight peak on the subject. Pitch alone is thus enough to distinguish the four utterance types tested. The PCA analysis was also used to identify higher-level trends in the data (principal components), two of which appear to correspond to core semantic properties, namely belief change and commitment. In addition to intonation, speaker commitment also correlates with utterance duration.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".