Verum Focus in Negative Yes / No Questions and Ladd's p / ¬p Ambiguity
Bibliographic record
Abstract
IntroductionThis paper is concerned with two generalizations involving negation in yes/n o (yn-)questions.The first generalization captures an interpretational difference cor related with preposed and non-preposed negation in yn-questions (Romero and Han, 2001).Preposed negation in yn-questions contributes the implicature that the speaker believed or at least expected that the positive answer is correct, as in (1) (Ladd, 1981;Han, 1998; Btiring and Gunlogson, 2000). 1 Non-preposed negation, instead, does not necessarily give rise to this implicature (Han, 1999): (2) can be a way of seeking information on whether John is a teetotaler.(1) Doesn't John drink?Positive epistemic implicature: The speaker believes or at least expects that John drinks.(2) Does John not drink?No epistemic implicature necessary.The contrast can be seen if we take a neutral, epistemically unbiased context like (3) and utter the two types of questions: (3S) can be understood in this context as an epistemically unbiased question, whereas (3S') necessarily conveys an epis temic bias of the speaker. 2 Example (4) also illustrates this interpretive difference.The resulting generalization is stated in (5).(3) Scenario: S hates both Pat and Jane.The prospect of an excursion without them pleases S. S does not have any previous belief about whether either of them is coming or not.A: Pat is not coming.S: Great !Is Jane not coming (either)?That would be the best!!! S': # Great !Isn't Jane coming
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".