Accommodating Presuppositions Is Inappropriate in Implausible Contexts
Bibliographic record
Abstract
According to one view of linguistic information (Karttunen, 1974; Stalnaker, 1974), a speaker can convey contextually new information in one of two ways: (a) by asserting the content as new information; or (b) by presupposing the content as given information which would then have to be accommodated. This distinction predicts that it is conversationally more appropriate to assert implausible information rather than presuppose it (e.g., von Fintel, 2008; Heim, 1992; Stalnaker, 2002). A second view rejects the assumption that presuppositions are accommodated; instead, presuppositions are assimilated into asserted content and both are correspondingly open to challenge (e.g., Gazdar, 1979; van der Sandt, 1992). Under this view, we should not expect to find a difference in conversational appropriateness between asserting implausible information and presupposing it. To distinguish between these two views of linguistic information, we performed two self-paced reading experiments with an on-line stops-making-sense judgment. The results of the two experiments-using the presupposition triggers the and too-show that accommodation is inappropriate (makes less sense) relative to non-presuppositional controls when the presupposed information is implausible but not when it is plausible. These results provide support for the first view of linguistic information: the contrast in implausible contexts can only be explained if there is a presupposition-assertion distinction and accommodation is a mechanism dedicated to reasoning about presuppositions.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".