Intonation, <i>yes</i> and <i>no</i>
Bibliographic record
Abstract
English polar particles yes and no are interchangeable in response to negative sentences, that is, either one can be used to convey both positive and negative responses. We provide a critical discussion of recent research into this phenomenon (Kramer & Rawlins 2009; Krifka 2013; Roelofsen & Farkas 2015; Holmberg 2016), which leads to three questions: Does the intonation produced on yes and no depend on whether the response is positive or negative, and can intonation affect the interpretation of bare polar particle responses? Which particles do speakers prefer to use when? Are preference patterns sensitive to the polarity of preceding sentences in the context? In a series of experiments, we demonstrate that the contradiction contour (Liberman & Sag 1974) is an intonation that is commonly produced on positive responses to negative sentences, and that it affects hearers’ interpretations of bare particle responses. Beyond intonation, our experimental results add new evidence regarding speakers’ preferences for using yes and no in response to negative polar questions and rising declaratives. Finally, our results suggest that preference patterns are not sensitive to the polarity of context sentences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".