Conversational Implicatures (and How to Spot Them)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In everyday conversations we often convey information that goes above and beyond what we strictly speaking say: exaggeration and irony are obvious examples. H.P. Grice introduced the technical notion of a conversational implicature in systematizing the phenomenon of meaning one thing by saying something else. In introducing the notion, Grice drew a line between what is said , which he understood as being closely related to the conventional meaning of the words uttered, and what is conversationally implicated , which can be inferred from the fact that an utterance has been made in context. Since Grice’s seminal work, conversational implicatures have become one of the major research areas in pragmatics. This article introduces the notion of a conversational implicature, discusses some of the key issues that lie at the heart of the recent debate, and explicates tests that allow us to reliably distinguish between semantic entailments and conventional implicatures on the one hand and conversational implicatures on the other.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it