Bibliographic record
Abstract
Rudin (2018) and Rudin & Rudin (2022) make a typological generalization that languages in which rising declaratives comprise non-canonical yes/no questions (YNQs), like English and Bulgarian, also allow for rising imperatives, used as tentative, but invested requests or disinterested suggestions, but languages in which rising declaratives comprise canonical YNQs, like Macedonian, don't allow for such rising imperatives. I look at another Slavic language, Russian, further expanding and fine-tuning the typology of how different languages realize various meaning components of different types of speech acts. While, like in Macedonian, Russian canonical YNQs are formed via an "intonation-only" strategy, said intonation doesn't involve a rising tune, but a special prosodic peak that I call the Q-Peak. I show that, despite marking canonical YNQs, the Q-Peak can also be used in friendly, but invested requests—but not in disinterested suggestions. I propose that the Q-Peak realizes an operator that asks the addressee to react to the speaker's speech act, which is appropriate in (some) questions and invested requests, but not in disinterested suggestions. The Russian Q-Peak is therefore distinct from the English-style rising tune, which in Rudin (& Rudin's) terms, simply "call[s] off the speaker's commitment to their utterance". The latter can thus have a wider range of meaning effects and brings a different source/flavor of politeness/tentativeness to directives.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".