Bibliographic record
Abstract
I am honored and flattered that this old text of mine should have been deemed worth translating and publishing in the Rhetoric Society Quarterly. It was initially intended as a chapter of my book Le symbolisme en général (Hermann, 1974; translated as Rethinking Symbolism by Alice L. Morton, for Cambridge University Press, 1975). But, under the encouragement of Tzetan Todorov, it developed beyond what I had planned and was taken out of the draft of the book. In 1975, Deirdre Wilson, who had introduced me to analytic philosophy in general and to the work of Paul Grice in particular, published her book, Presuppositions and Non-truth-conditional Semantics (Academic Press). She and I decided to write a joint programmatic paper covering the ground between semantics and the rhetoric of figures and we ended up collaborating for thirty years, and developing, with the help of many students and colleagues around the world, the cognitive approach to verbal communication known as Relevance Theory. In retrospect, my 1975 “rudiments” were indeed quite rudimentary. Still, re-reading the article, I confess that I find it insightful. Most insights have been integrated and improved upon in later work. Little has been done however with one of the main insights of the article: that the use of figures of speech evokes ideas not just about the topic of the utterance but also about the shared background knowledge of the interlocutors.—Dan Sperber, December 2006
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.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".